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1936 9120 M
("An Agent-Based Model of the Exchange Theory of Interest Groups") 4628 X
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3715 7632 M
(Paul E. Johnson) 1069 X
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3399 7446 M
(Dept. of Political Science) 1702 X
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3547 7260 M
(University of Kansas) 1406 X
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3410 7074 M
(Lawrence, Kansas 66045) 1679 X
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3730 6888 M
(\(785\) 864-9086) 1040 X
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3547 6702 M
(pauljohn@ukans.edu) 1405 X
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3968 5772 M
(Abstract) 564 X
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1000 5400 M
(This paper presents some preliminary results of a project designed to develop an\
individual-level, ) 6536 X
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1000 5214 M
(system-wide theory of interest group politics that extends Salisbury's exchange \
theory. The ) 6180 X
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1000 5028 M
(model is a combination of formal theory and computer simulation. The simulation\
is designed ) 6347 X
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1000 4842 M
(with the Swarm tookit which is made available by the Santa Fe Institute. The m\
odel describes a ) 6496 X
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1000 4656 M
(process in which recruiters take issue positions and contact citizens to ask the\
m to join. It ) 6032 X
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1000 4470 M
(investigates the implications of adding "incomplete information and rational ign\
orance" into the ) 6431 X
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1000 4284 M
(exchange theory.) 1137 X
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1000 2238 M
(Special thanks to Chris Langton, Glen E.P. Ropella, Marcus Daniels, Alex Lancast\
er, Rick ) 6094 X
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1000 2052 M
(Riolo, Benedikt Stefansson, Sven Thommesen, the Santa Fe Institute, and many oth\
ers in the ) 6238 X
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1000 1866 M
(Swarm Community. All remaining mistakes are the sole responsibility of the autho\
r. This ) 6053 X
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1000 1680 M
(research is partially supported by NSF grant SBR-9709404. Prepared for delivery\
at the 1998 ) 6303 X
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1000 1494 M
(Annual Meeting of the American Political Science Association, Boston Marriott Co\
pley Place ) 6312 X
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1000 1308 M
(and Sheraton Boston Hotel and Towers, September 3-6, 1998. ) 4183 X
GR
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7417 10600 M
(1) 83 X
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1500 9850 M
(This paper describes a modeling effort that uses the Swarm simulation toolkit to\
build a ) 5880 X
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1000 9664 M
(model of the development of an interest group system. The aim is to develop a s\
ystem-wide, ) 6243 X
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0.0000 GRAY
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1000 9478 M
(individual level theory that helps us to understand why some interest organizati\
ons grow and ) 6222 X
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0.0000 GRAY
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n
1000 9292 M
(some shrink, as well why some interests more well represented in politics than o\
thers. ) 5766 X
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0.0000 GRAY
GS
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1500 8920 M
(The substantive motivation for the project lies in the exchange theory of intere\
st group ) 5808 X
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0.0000 GRAY
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n
1000 8734 M
(politics \(Salisbury, 1969\). Membership in an organized interest group results\
from the ) 5817 X
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0.0000 GRAY
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1000 8548 M
(interaction of an interest organizer and prospective citizens. There are many s\
uch organizations, ) 6477 X
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0.0000 GRAY
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n
1000 8362 M
(however, each of which contacts many citizens. This project, which is by no mea\
ns complete, ) 6329 X
GR
0.0000 GRAY
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n
1000 8176 M
(describes individual citizens as passive, "rationally ignorant" actors, who may \
join organizations ) 6470 X
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1000 7990 M
(if they are contacted and offered a policy package of which they approve. Inter\
est organizations, ) 6504 X
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n
1000 7804 M
(which are represented by recruiters, make choices about what policy packages to \
offer and which ) 6523 X
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1000 7618 M
(citizens to contact. The recruiters described here do not interact with each ot\
her directly, but ) 6226 X
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0.0000 GRAY
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1000 7432 M
(they do have indirect contact in the sense that they may be competing to capture\
the loyalty of ) 6306 X
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0.0000 GRAY
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1000 7246 M
(the same citizens. Rather than focusing on the interaction of just one organiza\
tion and its ) 5998 X
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1000 7060 M
(members, the long range goal is to understand the patterns of development in a d\
iverse political ) 6406 X
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0.0000 GRAY
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n
1000 6874 M
(community in which there are many kinds of interest organizations. ) 4631 X
GR
0.0000 GRAY
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n
1500 6502 M
(The Swarm simulation toolkit is designed for the modeling of "complex adaptive ) 5442 X
GR
0.0000 GRAY
GS
n
1000 6316 M
(systems," models with large numbers of simultaneously interacting agents. The W\
WW site for ) 6389 X
GR
0.0000 GRAY
GS
n
1000 6130 M
(the Swarm project is http://www.santafe.edu /projects/swarm. Swarm provides a p\
rogramming ) 6396 X
GR
0.0000 GRAY
GS
n
1000 5944 M
(framework that uses the Objective-C language. Swarm is under the GNU Public Lice\
nse, which ) 6431 X
GR
0.0000 GRAY
GS
n
1000 5758 M
(means that it is "open source" software \(sometimes referred to as "free softwar\
e" in the sense that ) 6538 X
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0.0000 GRAY
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n
1000 5572 M
(anyone who purchases a product designed with software under the GPL has a right \
to receive the ) 6499 X
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0.0000 GRAY
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1000 5386 M
(source code for the project and also has the right to redistribute that source c\
ode. Like most ) 6168 X
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0.0000 GRAY
GS
n
1000 5200 M
(computer programming enterprises under the GPL, Swarm is an evolving set of code\
that ) 5994 X
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0.0000 GRAY
GS
n
1000 5014 M
(benefits from the contributions of support by educational institutions \(such as\
the Santa Fe ) 6068 X
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0.0000 GRAY
GS
n
1000 4828 M
(Institute\) and volunteers who submit code. Another paper presented at the APSA\
1998 meeting ) 6429 X
GR
0.0000 GRAY
GS
n
1000 4642 M
(\(Johnson, 1998\) describes the Swarm toolkit in greater depth. The simulations\
reported here ) 6219 X
GR
0.0000 GRAY
GS
n
1000 4456 M
(were done with Swarm 1.3, which was made available August 26, 1998. ) 4899 X
GR
0.0000 GRAY
GS
n
1500 4084 M
( This paper describes a work in progress. Many issues remain that need to be ) 5292 X
GR
0.0000 GRAY
GS
n
1000 3898 M
(incorporated into the computer model, and many details that are included in the \
code already ) 6224 X
GR
0.0000 GRAY
GS
n
1000 3712 M
(need to be investigated in greater depth. The plan is to first discuss the the\
oretical questions that ) 6521 X
GR
0.0000 GRAY
GS
n
1000 3526 M
(motivate the study, second to describe the computer model, and third to present \
some findings ) 6314 X
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0.0000 GRAY
GS
n
1000 3340 M
(and raise some new problems for research.) 2849 X
GR
0.0000 GRAY
GS
n
2744 2968 M
(The Exchange Theory and Complex Systems) 3012 X
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0.0000 GRAY
GS
n
1500 2596 M
(The logic of collective action \(Olson, 1965\) and the exchange theory of intere\
st groups ) 5873 X
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0.0000 GRAY
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n
1000 2410 M
(\(Salisbury, 1969\) provide the backdrop for the current investigation. Interes\
t group organizers--) 6413 X
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0.0000 GRAY
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n
1000 2224 M
(called entrepreneurs or recruiters--face the job of contacting citizens and pers\
uading them to join. ) 6621 X
GR
0.0000 GRAY
GS
n
1000 2038 M
(The theory holds that organizations offer selective incentives--either tangible \
"material" benefits ) 6467 X
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0.0000 GRAY
GS
n
1000 1852 M
(or intangible "expressive" benefits--that entice people to join. While many s\
tudies have ) 5981 X
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0.0000 GRAY
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1000 1666 M
(emphasized the fact that expressive benefits are the driving factor in many inte\
rest organizations, ) 6509 X
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1000 1480 M
(there is only scant formal theory that helps us to understand that process.) 4853 X
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7417 10600 M
(2) 83 X
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0.0000 GRAY
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1500 9850 M
(The exchange theory has fueled a number of productive investigations of the acti\
vities of ) 5975 X
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0.0000 GRAY
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n
1000 9664 M
(individual interest organizations. However, the exchange theory is, in its curr\
ent state of ) 5952 X
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0.0000 GRAY
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1000 9478 M
(development, not able to lead to understanding of system-wide political phenomen\
a. This point, ) 6468 X
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1000 9292 M
(made powerfully in recent research by Gray and Lowrey \(1996a\), presents us wit\
h a major ) 6082 X
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n
1000 9106 M
(challenge. If the exchange theory makes meaningful statements only about the mi\
cro-level ) 6115 X
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0.0000 GRAY
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1000 8920 M
(details of the interaction of one organizer and some citizens, where are we supp\
osed to turn for ) 6367 X
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0.0000 GRAY
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1000 8734 M
(broadly-applicable theories of representation and political change?) 4437 X
GR
0.0000 GRAY
GS
n
1500 8362 M
(Gray and Lowery have been the leading proponents in political science of a socio\
logical ) 5917 X
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n
1000 8176 M
(approach to organizations based on population ecology. Their approach emphasize\
s the ) 5904 X
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0.0000 GRAY
GS
n
1000 7990 M
(resources available to groups, which are thought as species that seek to ``consu\
me' members ) 6225 X
GR
0.0000 GRAY
GS
n
1000 7804 M
(from the general public and compete for policy influence \(1996a; 1996b; Lowery \
and Gray, ) 6156 X
GR
0.0000 GRAY
GS
n
1000 7618 M
(1993\). ``Perhaps the central proposition of population ecology is that environ\
ments determine ) 6336 X
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GS
n
1000 7432 M
(equilibria in terms of both absolute and relative numbers of individuals and spe\
cies.' \(Gray and ) 6393 X
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n
1000 7246 M
(Lowery, 1996a, p. 52\). Following the language of ecology, groups are assumed t\
o be in ) 5908 X
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0.0000 GRAY
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n
1000 7060 M
(equilibrium, occupying separated niches. ``A population ecology orientation lea\
ds us to assume ) 6450 X
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0.0000 GRAY
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1000 6874 M
(that interest organization density is set at an equilibrium level, albeit varyin\
g over time, by the ) 6316 X
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1000 6688 M
(environment.' \(Gray and Lowery, 1996a, p. 64\). ) 3301 X
GR
0.0000 GRAY
GS
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1500 6419 M
(The sociological adaptation of the ecological notions has some shortcomings. Mo\
st ) 5627 X
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1000 6233 M
(importantly, it does not have any individual-level underpinnings. My personal \
opinion is that ) 6318 X
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1000 6047 M
(any aggregate level theory that lacks a coherent explanation in terms of individ\
ual behavior is ) 6325 X
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0.0000 GRAY
GS
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1000 5861 M
(unsatisfactory. This shortcoming of the aggregate level analysis has been recog\
nized in ecology, ) 6503 X
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0.0000 GRAY
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1000 5675 M
(where more recent emphasis has been placed on understanding individual behavior/\
adaptation ) 6310 X
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1000 5476 M
(\() 56 X
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1056 5476 M
(Judson, 1992. ) 1109 X
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2165 5476 M
(DeAngelis and Gross, 1992; Ives, 1995; Kauffman, 1993; Schmitz and Booth, ) 5250 X
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1000 5290 M
(nd; Forrest and Jones, 1995; Jones, Hraber, and Forrest, 1996\). Many scholars \
agree on the ) 6153 X
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1000 5104 M
(desirability of building a bottom-up model because it better represents the intu\
ition that ) 5857 X
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1000 4918 M
(biological selection and evolution happen at the individual level. ) 4383 X
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1500 4546 M
( As an alternative to the sociological adaptation of the ecological metaphor, I \
choose ) 5673 X
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1000 4360 M
(instead to follow the lead of recent studies that have searched for individual l\
evel foundations ) 6274 X
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1000 4174 M
(from which to build system-wide models. The method of analysis is sometimes cal\
led "agent-) 6292 X
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1000 3988 M
(based modeling" \(Axelrod, 1997\) or "complex adaptive systems" theory \(CAS, a\
s in Waldrop, ) 6414 X
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0.0000 GRAY
GS
n
1000 3802 M
(1992; Holland, 1998\). A complex adaptive system describes a large number of i\
ndividuals ) 6147 X
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1000 3616 M
(whose behavior is responsive to their environment and each other. Since the ind\
ividuals are ) 6179 X
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1000 3430 M
(typically thought of as entities with limited amounts of information or for whom\
calculation is ) 6321 X
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0.0000 GRAY
GS
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1000 3244 M
(costly, his approach is naturally well suited to political models of interaction\
under conditions of ) 6472 X
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1000 3058 M
(limited information \(see Epstein and Axtell, 1996\) .) 3446 X
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1500 2686 M
(The system-wide implications of an agent-based model are sometimes called "emerg\
ent ) 5894 X
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n
1000 2500 M
(properties." An emergent property is an attribute of the aggregate model that is\
not obviously a ) 6363 X
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1000 2314 M
(result of the intentioned behavior of the individual agents. Examples of emerge\
nt properties, for ) 6472 X
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1000 2128 M
(example, are V-shaped duck formations, the regulation of temperature in a beehiv\
e by the ) 6024 X
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0.0000 GRAY
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1000 1942 M
(movement of the bees, or equilibrium in an exchange economy. In the last exampl\
e, each ) 6020 X
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1000 1756 M
(individual seeks only personal satisfaction, but under certain conditions the mu\
tual interaction ) 6309 X
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(may \(by an invisible hand\) lead to an equilibrium that balances supply and dem\
and.) 5576 X
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(3) 83 X
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1500 9850 M
(In the interest group context, what are the system-wide properties that we seek \
to ) 5445 X
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1000 9664 M
(understand? First and foremost, we want to understand the process through which\
the political ) 6364 X
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0.0000 GRAY
GS
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1000 9478 M
(viewpoints held by the citizens in a society are 'mobilized' into interest organ\
izations. The ) 6122 X
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0.0000 GRAY
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1000 9292 M
(aggregate phenomenon of representation, a situation in which there are interest \
organizations to ) 6409 X
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0.0000 GRAY
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1000 9106 M
(express certain points of view and their membership levels \(somehow\) mirror th\
e citizen-base, is ) 6494 X
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1000 8920 M
(an emergent property of a political system. This is worth understanding because\
the issue ) 6033 X
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0.0000 GRAY
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1000 8734 M
(priorities and decisions made in the government \(legislative, executive, or jud\
icial branches\) are ) 6443 X
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0.0000 GRAY
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1000 8548 M
(affected by the activities of organized interests \(or the failure of some inter\
ests to organize\). ) 6165 X
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0.0000 GRAY
GS
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1500 8176 M
(The model presented here investigates the implications of an exchange theory wit\
h ) 5558 X
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0.0000 GRAY
GS
n
1000 7990 M
(rational ignorance and incomplete information. Citizens are not well informed a\
bout the kinds of ) 6530 X
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0.0000 GRAY
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1000 7804 M
(organizations that exist. This results from their existence in a state of "rati\
onal ignorance". ) 6142 X
GR
0.0000 GRAY
GS
n
1000 7618 M
(Citizens may join organizations if they are personally contacted, but otherwise \
they do not. ) 6145 X
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0.0000 GRAY
GS
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1000 7432 M
(Before a citizen will join an organization, of course, there must be not only c\
ontact, but also an ) 6411 X
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0.0000 GRAY
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1000 7246 M
(attractive offer, something for which the citizen is willing to exchange money a\
nd time. The ) 6268 X
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0.0000 GRAY
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1000 7060 M
(problem of incomplete information is most important when we consider interest gr\
oup recruiters ) 6473 X
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n
1000 6874 M
(\(not citizens or prospective members\). The recruiters have an idea that some \
small number of ) 6311 X
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0.0000 GRAY
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1000 6688 M
(people would join if asked, but they do not know which ones would join. This in\
completeness of ) 6527 X
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0.0000 GRAY
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1000 6502 M
(information forces them to pay the high costs of contacting a lot of people who \
will not join in ) 6330 X
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0.0000 GRAY
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1000 6316 M
(order to find the ones who will.) 2095 X
GR
0.0000 GRAY
GS
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1500 5944 M
(While it is quite easy to model the exchange of material selective incentives, i\
t is more ) 5814 X
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0.0000 GRAY
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1000 5758 M
(difficult to model the exchange of expressive benefits. As Salisbury proposed i\
t , the exchange ) 6384 X
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0.0000 GRAY
GS
n
1000 5572 M
(theory specifies the idea that citizens may receive emotional satisfaction from \
feeling that they ) 6346 X
GR
0.0000 GRAY
GS
n
1000 5386 M
(have contributed to a cause with which they agree. However, a number of details\
are missing if ) 6422 X
GR
0.0000 GRAY
GS
n
1000 5200 M
(we are to formalize this theory. It is necessary to craft a more precise model\
to describe the way ) 6520 X
GR
0.0000 GRAY
GS
n
1000 5014 M
(interest group recruiters devise the "policy packages" that they offer to citize\
ns, as well as a ) 6146 X
GR
0.0000 GRAY
GS
n
1000 4828 M
(formalized model of the way in which citizens compare the many packages they mig\
ht receive ) 6342 X
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0.0000 GRAY
GS
n
1000 4642 M
(and decide whether to join any of the organizations. The model described here e\
xtends another ) 6396 X
GR
0.0000 GRAY
GS
n
1000 4456 M
(project \(Johnson, 1996\) by incorporating a multidimensional space as well as a\
multiplicity of ) 6303 X
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n
1000 4270 M
(recruiters.) 672 X
GR
0.0000 GRAY
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n
3529 3712 M
(Specifying the Model) 1442 X
GR
0.0000 GRAY
GS
n
1500 3340 M
(The computer code is written to allow as much generality as possible. Where po\
ssible, ) 5885 X
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0.0000 GRAY
GS
n
1000 3154 M
(design choices are made to allow the model to be scalable and investigated under\
a number of ) 6295 X
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0.0000 GRAY
GS
n
1000 2968 M
(conditions. There are many details to be specified in the design of a computer\
model. The ) 6141 X
GR
0.0000 GRAY
GS
n
1000 2782 M
(multiplicity of options makes this modeling approach powerful and yet, in some c\
ases, difficult ) 6392 X
GR
0.0000 GRAY
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n
1000 2596 M
(to manage and interpret. ) 1703 X
GR
0.0000 GRAY
GS
n
1500 2224 M
(There are three parts to the design of the model. First, there is code for indi\
vidual agents. ) 6055 X
GR
0.0000 GRAY
GS
n
1000 2038 M
(Second, there is code that creates a swarm of these agents and orchestrates thei\
r interaction. In ) 6374 X
GR
0.0000 GRAY
GS
n
1000 1852 M
(the Swarm parlance, that is called a "ModelSwarm." Third, there is an interface\
object that ) 6121 X
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0.0000 GRAY
GS
n
1000 1666 M
(manages the simulation and allows the user to monitor and interact with it. Thi\
s is called the ) 6230 X
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0.0000 GRAY
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1000 1480 M
("ObserverSwarm." These names are not required components of the simulation, but\
their usage ) 6431 X
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1000 1294 M
(makes it easier for Swarm programmers to discuss design questions among themselv\
es. ) 6002 X
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(The model has two kinds of agents, citizens and recruiters. Begin with the mode\
l of ) 5654 X
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1000 9646 M
(citizens. Each citizen has an ideal point x) 2788 X
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( ) 42 X
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4403 9646 M
(and a utility function U) 1552 X
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(i) 46 X
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6001 9646 M
(\(x\) which indicates ) 1318 X
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1000 9447 M
(the desirability of all policy offerings. The computer code is designed so that\
N can be adjusted ) 6434 X
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1000 9261 M
(to equal any positive integer. For purposes of creating graphs, of course, it i\
s most feasible to set ) 6517 X
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1000 9075 M
(N=1 or N=2. ) 943 X
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1500 8703 M
(The utility function used is the familiar Weighted Euclidean Distance model) 5085 X
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6585 8703 M
( \(Davis and ) 810 X
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1000 8517 M
(Hinich, 1966; Davis, Hinich, and Ordeshook, 1970\).) 3490 X
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4490 8517 M
( ) 42 X
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4532 8517 M
( Given a matrix of weights A) 1943 X
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6475 8476 M
(i) 46 X
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6521 8517 M
(, which ) 532 X
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1000 8331 M
(indicates the importance of each issue and its interaction with other issues, th\
is is written as ) 6138 X
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(*) 83 X
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(\)) 56 X
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1000 7463 M
(The matrix of weights can be adjusted to represent differences in tastes and emp\
hasis among ) 6215 X
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1000 7277 M
(voters. If the matrix A) 1529 X
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2529 7236 M
(i) 46 X
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2575 7277 M
( is the identity matrix, \(a matrix in which all coefficients are zero except ) 4854 X
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1000 7091 M
(the main diagonal, and those coefficients--a) 2914 X
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(i11) 212 X
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4126 7091 M
(,a) 116 X
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4242 7050 M
(i22) 212 X
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4454 7091 M
(,a) 116 X
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(i33) 212 X
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(, through a) 717 X
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(imm) 306 X
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(--) 112 X
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(equal 1\), then the ) 1198 X
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1000 6905 M
(voter's utility for a proposal depends solely on its distance from the ideal. F\
or) 5224 X
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6224 6905 M
( a more concrete ) 1149 X
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1000 6719 M
(example of a two dimensional model, where a policy offering consists of a pair,\
\(x) 5540 X
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6540 6678 M
(1) 83 X
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6623 6719 M
(,x) 125 X
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(2) 83 X
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(\). The ) 525 X
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1000 6533 M
(coefficients a) 893 X
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1893 6492 M
(i11) 212 X
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2105 6533 M
(, a) 158 X
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2263 6492 M
(i12) 212 X
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(, a) 158 X
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(i22) 212 X
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2845 6533 M
(, are chosen, and then the utility of a policy is given by its "Weighted ) 4650 X
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1000 6347 M
(Euclidean Distance" ) 1399 X
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1500 5444 M
(As described in the introduction of this paper, the citizen is thought of as a p\
assive entity, ) 5998 X
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1000 5258 M
(one that may join an organization if contacted. The additional structure inside\
an object of type ) 6408 X
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1000 5072 M
(citizen is used to determine the nature of the individual's response. Basically\
, the citizen will ) 6271 X
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1000 4886 M
(collect a list of all invitations received, and then evaluate them. A citizen's\
budget will restrict ) 6358 X
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1000 4700 M
(the number of invitations that can be accepted. Among the offers that are "tole\
rable" in their ) 6224 X
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1000 4514 M
(policy offering, the most attractive may be selected. There is another complica\
ting factor, ) 6048 X
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1000 4328 M
(however, which is that citizens may decide to free-ride and make not contributio\
n. ) 5569 X
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1500 3956 M
(Consider first the idea that the recruiter tries to get citizens to join the org\
anization. The ) 5930 X
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0.0000 GRAY
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1000 3770 M
(critical aspect of this effort is the ability to contact individuals and make th\
em offers. Suppose ) 6441 X
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0.0000 GRAY
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1000 3584 M
(that a recruiter contacts a citizen and conveys the message that the organizatio\
n is offering policy ) 6521 X
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1000 3380 M
(p) 83 X
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1083 3380 M
( \316) 161 X
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1244 3380 M
( R) 153 X
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1397 3421 M
(m) 130 X
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0.0000 GRAY
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1527 3380 M
( . A number of factors affect the citizen's response. First, the organization\
's policy ) 5668 X
GR
0.0000 GRAY
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1000 3181 M
(offering has to be "close enough" to the individual's ideal before the citizen w\
ill even give the ) 6308 X
GR
0.0000 GRAY
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1000 2995 M
(invitation serious consideration. Suppose each individual has a tolerance level\
, T) 5423 X
GR
0.0000 GRAY
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6423 2954 M
(i) 46 X
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0.0000 GRAY
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6469 2995 M
(, which ) 532 X
GR
0.0000 GRAY
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1000 2809 M
(indicates "how close" an organization's proposal must be to the citizen's ideal \
if that citizen is to ) 6485 X
GR
0.0000 GRAY
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1000 2623 M
(join up. The tolerance level is the maximum amount of policy discord that a per\
son can tolerate. ) 6528 X
GR
0.0000 GRAY
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1000 2437 M
(A necessary condition for joining an organization is that U) 3906 X
GR
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4906 2396 M
(i) 46 X
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4952 2437 M
(\(p\) > T) 475 X
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5427 2396 M
(i) 46 X
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5473 2437 M
(. T) 228 X
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5701 2396 M
(i) 46 X
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5747 2437 M
( can be thought of as an ) 1624 X
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0.0000 GRAY
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1000 2251 M
("exit level" of utility that a person can obtain by joining no organizations. ) 5026 X
GR
0.0000 GRAY
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n
1500 1879 M
(Not everyone who agrees with a cause will join, however. There are two reasons.\
Some ) 5961 X
GR
0.0000 GRAY
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1000 1693 M
(individuals may not have resources to contribute, and some who do may choose to \
free-ride on ) 6361 X
GR
0.0000 GRAY
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1000 1507 M
(the group's activities. Not everyone who agrees with a cause will feel the "wa\
rm glow" ) 5937 X
GR
0.0000 GRAY
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n
1000 1321 M
(\(Andreoni, 1988\) from contributing, in other words. A citizen who joined an o\
rganization in the ) 6469 X
GR
0.0000 GRAY
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1000 1135 M
(last time period may grow bored and resign, even though the organization's offer\
ings are just as ) 6436 X
GR
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(5) 83 X
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1000 9850 M
(appealing. These factors are taken into account by adding three parameters for \
each citizen. ) 6219 X
GR
0.0000 GRAY
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1000 9664 M
(First, there is a budget variable, b) 2231 X
GR
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3231 9623 M
(i) 46 X
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0.0000 GRAY
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3277 9664 M
(, which equals the number of organizations that a citizen can ) 4079 X
GR
0.0000 GRAY
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1000 9478 M
(afford to join. Second, there is a "free-rider coefficient." This is a number \
FR) 5232 X
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6232 9437 M
(i) 46 X
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0.0000 GRAY
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6278 9478 M
( in [0,1] which ) 1023 X
GR
0.0000 GRAY
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n
1000 9292 M
(determines the probability that the citizen will choose to not join an organizat\
ion that is ) 5855 X
GR
0.0000 GRAY
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1000 9106 M
(otherwise acceptable. Third, there is a "loyalty coefficient" L) 4110 X
GR
0.0000 GRAY
GS
n
5110 9065 M
(i) 46 X
GR
0.0000 GRAY
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n
5156 9106 M
( in [0,1]. The loyalty coefficient ) 2217 X
GR
0.0000 GRAY
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1000 8920 M
(gives the probability that a citizen will renew membership in an organization.) 5159 X
GR
0.0000 GRAY
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1500 8548 M
(In this model, the citizen collects a list of all invitations received during a \
time period \(in ) 5966 X
GR
0.0000 GRAY
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1000 8362 M
(the code, this is the "invitationsList"\). That list combines requests for memb\
ership renewal in ) 6304 X
GR
0.0000 GRAY
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1000 8176 M
(organizations to which the citizen currently belongs as well as requests to crea\
te new ) 5697 X
GR
0.0000 GRAY
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1000 7990 M
(memberships in various organizations. At the end of a time period, the citizen \
is told to process ) 6431 X
GR
0.0000 GRAY
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1000 7804 M
(the invitations and the following algorithm is conducted. Invitations from orga\
nizations whose ) 6379 X
GR
0.0000 GRAY
GS
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1000 7618 M
(policy offers are tolerable are added to a list of tolerable invitations and the\
other organizations ) 6373 X
GR
0.0000 GRAY
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1000 7432 M
(are deleted from the list of invitations and those organizations are sent reject\
ion or resignation ) 6304 X
GR
0.0000 GRAY
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1000 7246 M
(notices. Given a budget, b) 1780 X
GR
0.0000 GRAY
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n
2780 7205 M
(i) 46 X
GR
0.0000 GRAY
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n
2826 7246 M
(, the citizen sorts through the remaining offers to find the b) 3933 X
GR
0.0000 GRAY
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6759 7205 M
(i) 46 X
GR
0.0000 GRAY
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n
6805 7246 M
( most ) 408 X
GR
0.0000 GRAY
GS
n
1000 7060 M
(attractive organizations. This is implemented by repeatedly finding the most a\
ttractive invitation ) 6542 X
GR
0.0000 GRAY
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n
1000 6874 M
(in the list, removing it and adding it to a list of worthy organizations \(calle\
d the "bestList" in the ) 6452 X
GR
0.0000 GRAY
GS
n
1000 6688 M
(code\). That processing is repeated until the budget is exhausted. All remain\
ing invitations on ) 6349 X
GR
0.0000 GRAY
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1000 6502 M
(the "invitations list" are either sent a message that the individual will resign\
from that ) 5727 X
GR
0.0000 GRAY
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1000 6316 M
(organization or refuses to join, as is appropriate. ) 3300 X
GR
0.0000 GRAY
GS
n
1500 5944 M
(The list of worthy organizations is then processed from best to worst. This is\
where the ) 5929 X
GR
0.0000 GRAY
GS
n
1000 5758 M
(free-rider and loyalty coefficients take effect. If the citizen is not currentl\
y a member, and if a ) 6325 X
GR
0.0000 GRAY
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1000 5572 M
(draw from a uniform distribution on [0,1] is greater than FR) 4005 X
GR
0.0000 GRAY
GS
n
5005 5531 M
(i) 46 X
GR
0.0000 GRAY
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5051 5572 M
(, then the citizen ) 1142 X
GR
0.0000 GRAY
GS
n
6193 5572 M
(sends a message to ) 1306 X
GR
0.0000 GRAY
GS
n
1000 5386 M
(the organization that it will join \(and the recruiter adds that citizen to its \
list of members\). ) 6046 X
GR
0.0000 GRAY
GS
n
1000 5200 M
(Otherwise, a rejection message is sent. If the citizen is currently a member an\
d if the loyalty ) 6221 X
GR
0.0000 GRAY
GS
n
1000 5014 M
(coefficient L) 856 X
GR
0.0000 GRAY
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n
1856 4973 M
(i) 46 X
GR
0.0000 GRAY
GS
n
1902 5014 M
( is greater than a draw from a uniform distribution on [0,1], the citizen will r\
enew ) 5480 X
GR
0.0000 GRAY
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n
1000 4828 M
(membership when asked to do so. Otherwise the citizen will send a resignation me\
ssage.) 5907 X
GR
0.0000 GRAY
GS
n
1500 4456 M
(The model for the organizational recruiter has two substantively important sets \
of ) 5477 X
GR
0.0000 GRAY
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n
1000 4270 M
(routines. \(The rest of the code is bookkeeping.\) One set of routines govern\
s the contacting of ) 6353 X
GR
0.0000 GRAY
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n
1000 4084 M
(citizens and the other governs the adjustment of the organization's policy posit\
ion. As the ) 6099 X
GR
0.0000 GRAY
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1000 3898 M
(model is designed so far, the contact behavior is simple: the recruiter can cont\
act all citizens or a ) 6482 X
GR
0.0000 GRAY
GS
n
1000 3712 M
(randomly selected list of all citizens. The ability to make an offer to all \
citizens is better suited ) 6501 X
GR
0.0000 GRAY
GS
n
1000 3526 M
(to describe electoral competition than interest group recruitment, but this pos\
sibility is used as a ) 6470 X
GR
0.0000 GRAY
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n
1000 3340 M
(baseline to measure the effects of the recruiter's lack of information about whi\
ch citizens will ) 6279 X
GR
0.0000 GRAY
GS
n
1000 3154 M
(join. ) 342 X
GR
0.0000 GRAY
GS
n
1500 2782 M
(The working premise is that an organization begins with some supply of resources\
, ) 5570 X
GR
0.0000 GRAY
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n
1000 2596 M
(possibly obtained from a foundation \(Walker 1983\) or other sources. That init\
ial supply of ) 6106 X
GR
0.0000 GRAY
GS
n
1000 2410 M
(resources allows the organization to contact a fixed number of people for a fixe\
d number of time ) 6483 X
GR
0.0000 GRAY
GS
n
1000 2224 M
(periods. In the examples discussed here, each recruiter is given the resources \
to contact 10,000 ) 6391 X
GR
0.0000 GRAY
GS
n
1000 2038 M
(citizens during its first four time periods. After the initial periods are fini\
shed, then an ) 5803 X
GR
0.0000 GRAY
GS
n
1000 1852 M
(organization's recruitment level depends on its membership level. Presumably, e\
xisting ) 5917 X
GR
0.0000 GRAY
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n
1000 1666 M
(members pay dues and provide resources with which additional members are contact\
ed. The ) 6242 X
GR
0.0000 GRAY
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1000 1480 M
(number of citizens who are selected to be contacted in this second stage can be \
adjusted in the ) 6308 X
GR
0.0000 GRAY
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n
1000 1294 M
(model, but for the first runs, the number sampled is set equal to eight times th\
e current number of ) 6532 X
GR
0.0000 GRAY
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1000 1108 M
(members. This is an arbitrary setting based on empirical information about the \
volume of ) 6035 X
GR
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(6) 83 X
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1000 9850 M
(interest group recruiting and it certainly deserves to be "endogenized" as a var\
iable that the ) 6106 X
GR
0.0000 GRAY
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n
1000 9664 M
(recruiter can adjust. ) 1403 X
GR
0.0000 GRAY
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n
1500 9292 M
(One of the features that differentiates an interest organization's on-going recr\
uitment ) 5704 X
GR
0.0000 GRAY
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n
1000 9106 M
(activities from a political campaign is that the interest organization is able t\
o keep a list of ) 6035 X
GR
0.0000 GRAY
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n
1000 8920 M
(existing members and contact them to renew their membership. The existing membe\
rs have a ) 6313 X
GR
0.0000 GRAY
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n
1000 8734 M
(much higher probability of renewing than does a randomly drawn citizen of joinin\
g. As such, ) 6299 X
GR
0.0000 GRAY
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n
1000 8548 M
(the existing members are something of a "security blanket" for a recruiter. The\
models described ) 6529 X
GR
0.0000 GRAY
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n
1000 8362 M
(here all suppose that the recruiter always contacts existing members in addition\
to contacting ) 6233 X
GR
0.0000 GRAY
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n
1000 8176 M
(new prospects. This feature of automatically contacting existing members can be\
adjusted to ) 6240 X
GR
0.0000 GRAY
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n
1000 7990 M
(explore its implications. ) 1688 X
GR
0.0000 GRAY
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n
1500 7618 M
(How does this model of contacting match up with "the real world?" In reality, i\
nterest ) 5802 X
GR
0.0000 GRAY
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n
1000 7432 M
(organizations cannot contact all citizens, or even a comprehensive random sample\
. Instead, they ) 6532 X
GR
0.0000 GRAY
GS
n
1000 7246 M
(are forced either to throw recruitment messages at broad audiences through the m\
ass media or ) 6309 X
GR
0.0000 GRAY
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n
1000 7060 M
(they use direct mail advertising to contact subsets of citizens found through ma\
iling list brokers ) 6405 X
GR
0.0000 GRAY
GS
n
1000 6874 M
(or by sharing lists with other organizations. The model as now designed does no\
t incorporate ) 6276 X
GR
0.0000 GRAY
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n
1000 6688 M
(those details, it only allows random samples, but the code is designed for the n\
ext logical step, to ) 6540 X
GR
0.0000 GRAY
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n
1000 6502 M
(allow recruiters to exchange membership lists and draw samples from those lists.) 5406 X
GR
0.0000 GRAY
GS
n
1500 6130 M
(The second important component in the model of the recruiter is the ability to \
create a ) 5815 X
GR
0.0000 GRAY
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n
1000 5944 M
(policy offer that is made to the citizens. The recruiters are created with ran\
dom starting points. ) 6466 X
GR
0.0000 GRAY
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n
1000 5758 M
(After that, there are three possibilities for adjustment. First, a recruiter's \
position may be "stuck" ) 6520 X
GR
0.0000 GRAY
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1000 5572 M
(at the starting point. This exemplifies an organizational recruiter who begins \
with a proposition ) 6427 X
GR
0.0000 GRAY
GS
n
1000 5386 M
(and is stubborn about it. Second, a recruiter's proposal to new members may adj\
ust to suit the ) 6331 X
GR
0.0000 GRAY
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n
1000 5200 M
(interests of existing members. This is called the "median model" because the or\
ganization's ) 6193 X
GR
0.0000 GRAY
GS
n
1000 5014 M
(policy proposal is assumed to equal the multidimensional median of member ideal \
points. This ) 6402 X
GR
0.0000 GRAY
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n
1000 4828 M
(is a surrogate for politics inside the organization.) 3256 X
GR
0.0000 GRAY
GS
n
1500 4456 M
(The third model of position taking by the recruiter is also the most complicated\
. Suppose ) 6009 X
GR
0.0000 GRAY
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1000 4270 M
(that the recruiter adjusts the proposal in an effort to increase the number of \
members in the ) 6138 X
GR
0.0000 GRAY
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n
1000 4084 M
(organization. The recruiter always compares the total number of members after \
offering some ) 6381 X
GR
0.0000 GRAY
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n
1000 3898 M
(package p against the number obtained with the previous package, q. If the poli\
cy package p ) 6238 X
GR
0.0000 GRAY
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n
1000 3712 M
(causes membership to increase, then it will become the new baseline against whic\
h new ) 5897 X
GR
0.0000 GRAY
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n
1000 3526 M
(proposals are considered. ) 1773 X
GR
0.0000 GRAY
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n
1500 3154 M
(The model discussed here describes an organizational recruiter who makes increme\
ntal ) 5853 X
GR
0.0000 GRAY
GS
n
1000 2968 M
(adjustments when making offers to the citizens. Proposals are thought of as res\
ults of steps in ) 6313 X
GR
0.0000 GRAY
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n
1000 2782 M
(directions in the policy space. The initial position and the distance and direc\
tion of movement in ) 6500 X
GR
0.0000 GRAY
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n
1000 2596 M
(the first proposal are determined according to some random distribution. If a s\
tep results in an ) 6341 X
GR
0.0000 GRAY
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n
1000 2410 M
(improvement in membership levels, then the recruiter's next proposal continues i\
n exactly that ) 6346 X
GR
0.0000 GRAY
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n
1000 2224 M
(direction. The trigonometry is generalizable to N dimensions, but consider this \
two-dimensional ) 6446 X
GR
0.0000 GRAY
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1000 2038 M
(example. If q=\(q) 1160 X
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2160 1997 M
(1) 83 X
GR
0.0000 GRAY
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n
2243 2038 M
(+q) 177 X
GR
0.0000 GRAY
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n
2420 1997 M
(2) 83 X
GR
0.0000 GRAY
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n
2503 2038 M
(\) is the current position, and a move of distance d is considered in the ) 4664 X
GR
0.0000 GRAY
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1000 1785 M
(direction ) 633 X
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1647 1828 M
(Q) 124 X
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0.0000 GRAY
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1757 1785 M
( then the resulting proposal, p=\(p) 2212 X
GR
0.0000 GRAY
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3969 1744 M
(1) 83 X
GR
0.0000 GRAY
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n
4052 1785 M
(,p) 125 X
GR
0.0000 GRAY
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n
4177 1744 M
(2) 83 X
GR
0.0000 GRAY
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n
4260 1785 M
(\) is) 209 X
GR
0.0000 GRAY
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n
1000 1532 M
( p) 671 X
GR
0.0000 GRAY
GS
n
1671 1491 M
(1) 83 X
GR
0.0000 GRAY
GS
n
1754 1532 M
( = q) 303 X
GR
0.0000 GRAY
GS
n
2057 1491 M
(1) 83 X
GR
0.0000 GRAY
GS
n
2140 1532 M
( + d * cos\() 706 X
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n
2860 1575 M
(Q) 124 X
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0.0000 GRAY
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2970 1532 M
(\)) 56 X
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0.0000 GRAY
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n
1000 1279 M
( p) 671 X
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0.0000 GRAY
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n
1671 1238 M
(2 ) 125 X
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0.0000 GRAY
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n
1796 1279 M
(= q) 261 X
GR
0.0000 GRAY
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n
2057 1238 M
(2 ) 125 X
GR
0.0000 GRAY
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n
2182 1279 M
(+ d * sin\() 636 X
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n
2832 1322 M
(Q) 124 X
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(\)) 56 X
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(7) 83 X
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1000 9664 M
(The distance of the proposed changed, is chosen at random from a Uniform Distri\
bution on ) 6168 X
GR
0.0000 GRAY
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1000 9478 M
([0,max]. The longest possible step, max, can be specified at run-time by the us\
er. ) 5533 X
GR
0.0000 GRAY
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n
1500 9106 M
(If a proposal results in a net reduction in the organization's membership level,\
then the ) 5820 X
GR
0.0000 GRAY
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n
1000 8920 M
(recruiter considers variations in the direction of movement away from the policy\
q. The ) 5965 X
GR
0.0000 GRAY
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1000 8734 M
(working hypothesis is that a new proposal ought most likely proceed in the same \
direction as ) 6233 X
GR
0.0000 GRAY
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n
1000 8548 M
(previous change. At any given stage, the direction of the next proposed change i\
s found by taking ) 6542 X
GR
0.0000 GRAY
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1000 8295 M
(the current direction, ) 1434 X
GR
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2427 8520 2547 8306 DOCLIPBOX
n
2448 8338 M
(Q) 124 X
GR
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0.0000 GRAY
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n
2558 8295 M
(, and adding a displacement, ) 1938 X
GR
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4494 8520 4588 8306 DOCLIPBOX
n
4510 8338 M
(D) 102 X
GR
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0.0000 GRAY
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n
4598 8295 M
(. To capture this logic, the displacement ) 2748 X
GR
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n
7360 8338 M
(D) 102 X
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0.0000 GRAY
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n
7448 8295 M
( ) 42 X
GR
0.0000 GRAY
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1000 8042 M
(is drawn from a truncated Normal Distribution with a mean of 0. The distributio\
n of ) 5687 X
GR
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n
6701 8085 M
(D) 102 X
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0.0000 GRAY
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6789 8042 M
( puts more ) 746 X
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0.0000 GRAY
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1000 7789 M
(weight on 0 than any other value. The direction ) 3241 X
GR
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n
4255 7832 M
(Q) 124 X
GR
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0.0000 GRAY
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n
4365 7789 M
( is measured in radians, so a change of -1 ) 2793 X
GR
0.0000 GRAY
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1000 7603 M
(means a complete reversal in direction. If the agenda setter is intended to move\
only in a ) 5941 X
GR
0.0000 GRAY
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1000 7350 M
("forward" direction, the value of ) 2209 X
GR
GS
0.0000 GRAY
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3200 7573 3294 7359 DOCLIPBOX
n
3223 7393 M
(D) 102 X
GR
GR
0.0000 GRAY
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n
3311 7350 M
( can be restricted to fall between \(-.5,+.5\). This is done by ) 3909 X
GR
0.0000 GRAY
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1000 7164 M
(truncating a Normal distribution at \(-2,2\) and then rescaling so it fits into \
the \(-.5,.+5\) range. If ) 6394 X
GR
0.0000 GRAY
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1000 6911 M
(the proposal is allowed to completely reverse itself, then ) 3802 X
GR
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n
4816 6954 M
(D) 102 X
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0.0000 GRAY
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n
4904 6911 M
( would be restricted to fall between \(-) 2503 X
GR
0.0000 GRAY
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1000 6725 M
(1,+1\). ) 442 X
GR
0.0000 GRAY
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n
1500 6353 M
(The individual level objects described thus far are created by and interact acco\
rding to the ) 6035 X
GR
0.0000 GRAY
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1000 6167 M
(instructions of higher-level objects, the ModelSwarm and the ObserverSwarm. A n\
umber of ) 6225 X
GR
0.0000 GRAY
GS
n
1000 5981 M
(parameters are set in the ModelSwarm that can be investigated. For example, the\
Swarm toolkit ) 6461 X
GR
0.0000 GRAY
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n
1000 5795 M
(includes many random number generation objects and statistical distributions. I\
n the models ) 6225 X
GR
0.0000 GRAY
GS
n
1000 5609 M
(described here, the ideal points have been assigned randomly \(and independentl\
y\) according to a ) 6520 X
GR
0.0000 GRAY
GS
n
1000 5423 M
(Multivariate Normal Distribution with a mean of 50, a standard deviaition of 20,\
and covariance ) 6451 X
GR
0.0000 GRAY
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n
1000 5237 M
(set equal to zero. I have only considered "circular preferences," ones for whic\
h the weight ) 6078 X
GR
0.0000 GRAY
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n
1000 5051 M
(matrix A) 597 X
GR
0.0000 GRAY
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n
1597 5010 M
(i) 46 X
GR
0.0000 GRAY
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n
1643 5051 M
( is the identity matrix. For the most part, the exit level utility values, T) 4770 X
GR
0.0000 GRAY
GS
n
6413 5010 M
(i) 46 X
GR
0.0000 GRAY
GS
n
6459 5051 M
(, have been ) 796 X
GR
0.0000 GRAY
GS
n
1000 4865 M
(kept fixed at a common value for all citizens. The free rider and loyalty coeff\
icients can be set at ) 6521 X
GR
0.0000 GRAY
GS
n
1000 4679 M
(run-time. Most work done so far has set FR=0 and L=1, so that the random fluctu\
ations in ) 6085 X
GR
0.0000 GRAY
GS
n
1000 4493 M
(membership due to those factors are kept to a minimum. Future investigation w\
ill of course ) 6262 X
GR
0.0000 GRAY
GS
n
1000 4307 M
(explore many possibilities. Along the lines of KMP, one wonders what happens if\
the people ) 6271 X
GR
0.0000 GRAY
GS
n
1000 4121 M
(with extreme attitudes on one dimension are also the people who place very littl\
e weight on the ) 6384 X
GR
0.0000 GRAY
GS
n
1000 3935 M
(other issues.) 824 X
GR
0.0000 GRAY
GS
n
1500 3563 M
(There are number of other simulation modeling decisions must be made to rough o\
ut the ) 5978 X
GR
0.0000 GRAY
GS
n
1000 3377 M
(basic structure of the model. For example, should the population of citizens be\
elaborated to ) 6217 X
GR
0.0000 GRAY
GS
n
1000 3191 M
(include the birth, aging, and death of citizens? While it would be relatively e\
asy to introduce ) 6245 X
GR
0.0000 GRAY
GS
n
1000 3005 M
(turnover, the current strategy is to keep the list of citizens fixed or order to\
focus on other ) 6002 X
GR
0.0000 GRAY
GS
n
1000 2819 M
(variables of interest. ) 1440 X
GR
0.0000 GRAY
GS
n
1500 2447 M
(By far the most ticklish questions concern the assumptions about the creation an\
d ) 5471 X
GR
0.0000 GRAY
GS
n
1000 2261 M
(destruction of interest group recruiter objects in the model. When should a rec\
ruiter die? How ) 6375 X
GR
0.0000 GRAY
GS
n
1000 2075 M
(often should they be inserted into the model?) 3000 X
GR
0.0000 GRAY
GS
n
1500 1703 M
(The rest of the coding exercise is focused on making the simulation generate mea\
ningful ) 5949 X
GR
0.0000 GRAY
GS
n
1000 1517 M
(diagnostic information and graphs. Recent changes in the Swarm toolkit make som\
ewhat nicer ) 6388 X
GR
0.0000 GRAY
GS
n
1000 1331 M
(looking "ZoomRaster" graphs \(2 dimensional grids upon which one can plot the po\
sitions of the ) 6438 X
GR
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(8) 83 X
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1000 9850 M
(citizens and the interest group recruiters\) and the EZGraphs that depict time-s\
eries data have ) 6209 X
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1000 9664 M
(been enhanced. ) 1068 X
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0.0000 GRAY
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2907 9292 M
(Exploring the Implications of the Model) 2686 X
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0.0000 GRAY
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1500 8920 M
(In its current state of development, the model allows some insights but mostly i\
t leads to ) 5935 X
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1000 8734 M
(more questions. ) 1181 X
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1500 8362 M
(What is the effect of limited information? ) 2818 X
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4318 8362 M
( If an interest group recruiter could ) 2375 X
GR
0.0000 GRAY
GS
n
1000 8176 M
(communicate directly with each and every citizen, we expect membership to be hig\
her than if ) 6281 X
GR
0.0000 GRAY
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1000 7990 M
(only a subset could be contacted. The interesting questions concern the effect \
of this "slippage" ) 6430 X
GR
0.0000 GRAY
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1000 7804 M
(from potential membership to actual membership on the representative nature of t\
he interest ) 6177 X
GR
0.0000 GRAY
GS
n
1000 7618 M
(group system. There are also interesting effects is seen, however, when the coe\
fficients the ) 6138 X
GR
0.0000 GRAY
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1000 7432 M
(govern free riding and loyalty are varied. ) 2776 X
GR
0.0000 GRAY
GS
n
1500 7060 M
(Begin with a set of five interest group recruiters whose positions are permanent\
ly fixed. ) 5932 X
GR
0.0000 GRAY
GS
n
1000 6874 M
(The population size is set at 100,000 \(for no particular reason except time con\
straints. These ) 6195 X
GR
0.0000 GRAY
GS
n
1000 6688 M
(simulations typically take a long time!\). Each recruiter is allowed to contact\
10,000 people ) 6118 X
GR
0.0000 GRAY
GS
n
1000 6502 M
(chosen at random in each of the first four time periods, and then the number eac\
h recruiter may ) 6393 X
GR
0.0000 GRAY
GS
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1000 6316 M
(contact is equal to eight times its number of members. These models can be run \
with the free ) 6277 X
GR
0.0000 GRAY
GS
n
1000 6130 M
(rider coefficient set to 0 and the loyalty coefficient is set to 1, so there is \
no unpredictable ) 6009 X
GR
0.0000 GRAY
GS
n
1000 5944 M
(behavior. For each organization, a "potential membership" level is calculated b\
y asking each ) 6250 X
GR
0.0000 GRAY
GS
n
1000 5758 M
(citizen if that organization's policy offering is tolerable. In essence, potent\
ial membership is the ) 6472 X
GR
0.0000 GRAY
GS
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1000 5572 M
(highest possible membership an organization can expect to achieve if its positio\
n if fixed and if it ) 6525 X
GR
0.0000 GRAY
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1000 5386 M
(competes with no other organizations for members.) 3429 X
GR
0.0000 GRAY
GS
n
1500 5014 M
(Figure 1 presents a screenshot of a simulation in which there are five recruiter\
s, whose ) 5814 X
GR
0.0000 GRAY
GS
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1000 4828 M
(policy positions are chosen at random from the same Normal Distribution from whi\
ch citizens ) 6320 X
GR
0.0000 GRAY
GS
n
1000 4642 M
(are drawn. In the bottom left one can see the parameter settings that governed t\
he creation of the ) 6464 X
GR
0.0000 GRAY
GS
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1000 4456 M
(model. Some of these parameters--the ones that govern changes of position by th\
e recruiter--are ) 6472 X
GR
0.0000 GRAY
GS
n
1000 4270 M
(irrelevant in this case since the recruiter's policy offering is fixed. As indi\
cated by the other ) 6191 X
GR
0.0000 GRAY
GS
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1000 4084 M
(coefficients, the citizens in this model are never free riders \(FR) 4209 X
GR
0.0000 GRAY
GS
n
5209 4043 M
(i) 46 X
GR
0.0000 GRAY
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5255 4084 M
(=0\) and furthermore they are ) 1970 X
GR
0.0000 GRAY
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1000 3898 M
(highly loyal \(L) 998 X
GR
0.0000 GRAY
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1998 3857 M
(i) 46 X
GR
0.0000 GRAY
GS
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2044 3898 M
(=1\). In this setting, the key question is "how long does it take for the rando\
m ) 5200 X
GR
0.0000 GRAY
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1000 3712 M
(sampling process take to find each and every citizen." Once they are recruited,\
citizens stay until ) 6521 X
GR
0.0000 GRAY
GS
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1000 3526 M
(they receive a better policy offer from another organization, so a membership ba\
se naturally ) 6178 X
GR
0.0000 GRAY
GS
n
1000 3340 M
(accrues. The top part of the figure shows the membership rates of the organizat\
ional recruiters ) 6365 X
GR
0.0000 GRAY
GS
n
1000 3154 M
(and the bottom left shows a grid that describes the positions of the citizens \
\(dark dots\) and the ) 6300 X
GR
0.0000 GRAY
GS
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1000 2968 M
(policy offers made by the interest organizers \(M0,M1,M2,M3,M4\). ) 4616 X
GR
0.0000 GRAY
GS
n
1500 2596 M
(Since each recruiter's policy offer is a fixed quantity, the interesting aspect \
of this graph ) 5923 X
GR
0.0000 GRAY
GS
n
1000 2410 M
(is that the membership of all organizations gradually grows to its potential. \
The model allows ) 6355 X
GR
0.0000 GRAY
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1000 2224 M
(each organization to contact 10,000 members in the first four periods, and this \
somewhat ) 5968 X
GR
0.0000 GRAY
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1000 2038 M
(obscures the advantage held by organizations with a large potential membership. \
Organizations ) 6478 X
GR
0.0000 GRAY
GS
n
1000 1852 M
(that have more members can make more contacts, so they grow more quickly and mob\
ilize a ) 6225 X
GR
0.0000 GRAY
GS
n
1000 1666 M
(higher fraction of their potential membership at any given time.) 4237 X
GR
0.0000 GRAY
GS
n
1500 1294 M
(Figure 2 shows what happens when the possibilities of free-riding and disloyalty\
are ) 5656 X
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1000 1108 M
(introduced. In this model, the free-rider coefficient is chosen from a Uniform \
Distribution ) 6073 X
GR
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7417 10600 M
(9) 83 X
GR
0.0000 GRAY
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1000 9850 M
([0,0.5]. This coefficient is put into the agent at the time of creation and rem\
ains fixed throughout ) 6528 X
GR
0.0000 GRAY
GS
n
1000 9664 M
(its lifetime. The free-rider coefficient, FR) 2817 X
GR
0.0000 GRAY
GS
n
3817 9623 M
(i, ) 130 X
GR
0.0000 GRAY
GS
n
3947 9664 M
(is the probability that the individual will refuse to ) 3341 X
GR
0.0000 GRAY
GS
n
1000 9478 M
(join even though the organization's offering is tolerable. The loyalty coeffici\
ent is also set at ) 6248 X
GR
0.0000 GRAY
GS
n
1000 9292 M
(creation time. It is chosen from a Uniform Distribution [0.6,0.9]. L) 4543 X
GR
0.0000 GRAY
GS
n
5543 9251 M
(i) 46 X
GR
0.0000 GRAY
GS
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5589 9292 M
( is the probability that the ) 1747 X
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0.0000 GRAY
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1000 9106 M
(individual citizen will renew an organizational membership if the organization i\
s in the ) 5838 X
GR
0.0000 GRAY
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1000 8920 M
(individual's list of worthwhile alternatives. In other words, \(1-L) 4307 X
GR
0.0000 GRAY
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5307 8879 M
(i) 46 X
GR
0.0000 GRAY
GS
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5353 8920 M
(\) is the probability that an ) 1757 X
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0.0000 GRAY
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1000 8734 M
(individual will resign from an organization even if its policy offering is still\
acceptable.) 5826 X
GR
0.0000 GRAY
GS
n
1500 8362 M
(The advantage of being a recruiter with a higher potential membership is the mai\
n point ) 5897 X
GR
0.0000 GRAY
GS
n
1000 8176 M
(illustrated in Figure 2. When citizens are fickle, membership levels are lower.\
But there is a ) 6235 X
GR
0.0000 GRAY
GS
n
1000 7990 M
(vital threshold effect illustrated here. Organizations with potential membersh\
ip of 3180 or less ) 6411 X
GR
0.0000 GRAY
GS
n
1000 7804 M
(see their membership level collapse. Recruiters 2 and 4, in fact, drop below 10\
members and the ) 6494 X
GR
0.0000 GRAY
GS
n
1000 7618 M
(simulation removes them from the program at that time. Recruiter 0 seems to be \
headed in that ) 6402 X
GR
0.0000 GRAY
GS
n
1000 7432 M
(direction. In contrast, organizations with higher potential membership levels s\
urvive and ) 5984 X
GR
0.0000 GRAY
GS
n
1000 7246 M
(develop relatively stable membership levels. The advantage of having a large p\
otential ) 5896 X
GR
0.0000 GRAY
GS
n
1000 7060 M
(membership is clearly seen in the differences between Recruiters 1 and 3. Recru\
iter 3's potential ) 6521 X
GR
0.0000 GRAY
GS
n
1000 6874 M
(membership level, 4601, is slightly more than 10 percent higher than that of rec\
ruiter 1, which is ) 6477 X
GR
0.0000 GRAY
GS
n
1000 6688 M
(4179. The membership level of recruiter 3's organization averages approximately\
910 in the last ) 6505 X
GR
0.0000 GRAY
GS
n
1000 6502 M
(ten time periods shown, while that of recruiter 1 is about 570. In other words,\
a ten percent ) 6136 X
GR
0.0000 GRAY
GS
n
1000 6316 M
(increase in membership potential converts itself into a 59 percent increase in m\
embership.) 6010 X
GR
0.0000 GRAY
GS
n
1500 5944 M
(Of course, a number of factors could alter this outcome. For example, the indus\
trious ) 5759 X
GR
0.0000 GRAY
GS
n
1000 5758 M
(recruiter 1 might increase the volume of recruitment activity. I am studying at\
this moment a ) 6257 X
GR
0.0000 GRAY
GS
n
1000 5572 M
(way to allow the recruiters to adjust their recruitment activity to counteract t\
hese problems. Still, ) 6522 X
GR
0.0000 GRAY
GS
n
1000 5386 M
(I think the major point here is important. Assumptions at the individual level \
about the ) 5849 X
GR
0.0000 GRAY
GS
n
1000 5200 M
(distribution of tastes and at the organizational level about recruiting have imp\
ortant implications ) 6457 X
GR
0.0000 GRAY
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n
1000 5014 M
(for the overall makeup we expect to develop in interest group politics.) 4670 X
GR
0.0000 GRAY
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n
1500 4642 M
(What is the effect of crowding in the previous story?) 3481 X
GR
0.0000 GRAY
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4981 4642 M
( ) 84 X
GR
0.0000 GRAY
GS
n
1500 4270 M
(One attractive feature of the simulation toolkit is that the seed value for rand\
om numbers ) 5963 X
GR
0.0000 GRAY
GS
n
1000 4084 M
(can be set in the code so that the exact same random number stream can be invest\
igated under ) 6302 X
GR
0.0000 GRAY
GS
n
1000 3898 M
(different institutional conditions. The luck of the draw in Figures 1 and 2 gav\
e us recruiters ) 6162 X
GR
0.0000 GRAY
GS
n
1000 3712 M
(whose positions are relatively evenly spaced. Each citizen is willing to tolera\
te a group's policy ) 6450 X
GR
0.0000 GRAY
GS
n
1000 3526 M
(proposal if it is within 10 units of its ideal, a fact which means that if organ\
izations are relatively ) 6485 X
GR
0.0000 GRAY
GS
n
1000 3340 M
(far apart in the space, they don't compete for members. \(We might as well make\
five one-group ) 6473 X
GR
0.0000 GRAY
GS
n
1000 3154 M
(models, a critic might observe.\)) 2111 X
GR
0.0000 GRAY
GS
n
1500 2782 M
(At run-time, the number of recruiters can be changed, and/or the simulation code\
can be ) 5909 X
GR
0.0000 GRAY
GS
n
1000 2596 M
(written so that organizations are created and removed from the simulation. To i\
llustrate the ) 6144 X
GR
0.0000 GRAY
GS
n
1000 2410 M
(effect of crowding, Figure 3 shows what happens if there are 35 new recruiters a\
re added to the 5 ) 6517 X
GR
0.0000 GRAY
GS
n
1000 2224 M
(from the previous example. There is no free-riding or disloyalty, as the parame\
ter settings ) 6069 X
GR
0.0000 GRAY
GS
n
1000 2038 M
(indicate. The budget of each citizen is still set equal to 1, so citizens are f\
orced to make a choice ) 6491 X
GR
0.0000 GRAY
GS
n
1000 1852 M
(when they receive several attractive offers. ) 2944 X
GR
0.0000 GRAY
GS
n
1500 1480 M
(The differences between Figure 3 and Figures 1 and 2 are stark. Simply put, the\
) 5398 X
GR
0.0000 GRAY
GS
n
1000 1294 M
(possibility that organizations are forced to compete for their membership base w\
ith similar ) 6084 X
GR
0.0000 GRAY
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n
1000 1108 M
(organizations breaks the simple relationship that previously existed between pot\
ential ) 5740 X
GR
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7334 10600 M
(10) 166 X
GR
0.0000 GRAY
GS
n
1000 9850 M
(membership and observed membership levels. A scatterplot showing the relations\
hip between ) 6383 X
GR
0.0000 GRAY
GS
n
1000 9664 M
("potential membership" and actual membership after 50 periods is presented in Fi\
gure 4. The ) 6291 X
GR
0.0000 GRAY
GS
n
1000 9478 M
(relationship is certainly not tight enough to support a theoretical statement th\
at the membership ) 6389 X
GR
0.0000 GRAY
GS
n
1000 9292 M
(in organized interests is proportional to the interest and support in their posi\
tions held by the ) 6195 X
GR
0.0000 GRAY
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1000 9106 M
(general public. ) 1031 X
GR
0.0000 GRAY
GS
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1500 8734 M
(One of the most pressing task is to build a more complete model of the logic tha\
t governs ) 5996 X
GR
0.0000 GRAY
GS
n
1000 8548 M
(the creation and death of organizations. In this model, no organizations are b\
orn after time ) 6140 X
GR
0.0000 GRAY
GS
n
1000 8362 M
(period 0 and they die only when their membership drops below 10. Under those co\
nditions, it ) 6312 X
GR
0.0000 GRAY
GS
n
1000 8176 M
(does not appear that groups inhabit isolated niches in the policy space. If the\
re were some entity ) 6480 X
GR
0.0000 GRAY
GS
n
1000 7990 M
(that could stop organizations from taking issue stances that are close to each o\
ther, then this ) 6156 X
GR
0.0000 GRAY
GS
n
1000 7804 M
(crowding effect would be ameliorated and the relationship between potential memb\
ership and ) 6297 X
GR
0.0000 GRAY
GS
n
1000 7618 M
(observed membership would reappear. The problem, however, as evidenced Figure 3\
, is that ) 6248 X
GR
0.0000 GRAY
GS
n
1000 7432 M
(several organizations can indeed exist in very close proximity. In the end, the\
y compete for ) 6162 X
GR
0.0000 GRAY
GS
n
1000 7246 M
(members, and until some of the recruiters decide they will close-up shop, there'\
s no reason to ) 6276 X
GR
0.0000 GRAY
GS
n
1000 7060 M
(expect this will change. The models discussed in the next section--quite by acc\
ident--shed some ) 6475 X
GR
0.0000 GRAY
GS
n
1000 6874 M
(light on this problem.) 1433 X
GR
0.0000 GRAY
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GS
n
1500 6316 M
(What if policy offerings can be changed?) 2735 X
GR
0.0000 GRAY
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GS
n
1500 5944 M
(The models described so far put organizations at fixed positions. In reality, a\
n interest ) 5807 X
GR
0.0000 GRAY
GS
n
1000 5758 M
(organization's policy stances can change. I've developed two models of policy c\
hange, the ) 6132 X
GR
0.0000 GRAY
GS
n
1000 5572 M
("median model" of democratically governed groups and the "trajectory model" of t\
he ) 5721 X
GR
0.0000 GRAY
GS
n
1000 5386 M
(membership-maximizing recruiter. ) 2408 X
GR
0.0000 GRAY
GS
n
1500 5014 M
(If there is only a single recruiter, either type of recruiter position-taking us\
ually leads the ) 5959 X
GR
0.0000 GRAY
GS
n
1000 4828 M
(position into the center of the policy spectrum. A large number of replications\
\(not done yet\) ) 6254 X
GR
0.0000 GRAY
GS
n
1000 4642 M
(will be needed to quantity the chances that an organization that follows the med\
ian voter will ) 6239 X
GR
0.0000 GRAY
GS
n
1000 4456 M
(move its policy to an outer extreme. \(As indicated in Johnson, 1996, it is pos\
sible for this to ) 6199 X
GR
0.0000 GRAY
GS
n
1000 4270 M
(happen if the random draw of initial members has a median that is further away f\
rom the center ) 6407 X
GR
0.0000 GRAY
GS
n
1000 4084 M
(than the mean of ideal points. Given that the distribution of citizen ideal poi\
nts is Normal, this is ) 6506 X
GR
0.0000 GRAY
GS
n
1000 3898 M
(unlikely\). All of the examples I have seen thus far are similar to the display\
s in Figure 5 \(for the ) 6481 X
GR
0.0000 GRAY
GS
n
1000 3712 M
(median model\) and Figure 6 \(for the trajectory model\). The policy position o\
f the organization is ) 6533 X
GR
0.0000 GRAY
GS
n
1000 3526 M
(labeled "M0" in the median model and "T0" in the trajectory model. The organizat\
ion's position ) 6450 X
GR
0.0000 GRAY
GS
n
1000 3340 M
(starts at the extreme right in this example. In the Proposal Display \(bottom l\
eft panel of each ) 6252 X
GR
0.0000 GRAY
GS
n
1000 3154 M
(figure\), the history of the policy positions that the organization takes is sho\
wn. In a corporatist ) 6369 X
GR
0.0000 GRAY
GS
n
1000 2968 M
(society, where all citizens are allowed to join only one organization, we should\
expect that ) 6069 X
GR
0.0000 GRAY
GS
n
1000 2782 M
(organization's policy to be in the center of the preference distribution \(as Ol\
son \(1982\) ) 5828 X
GR
0.0000 GRAY
GS
n
1000 2596 M
(expected\). ) 731 X
GR
0.0000 GRAY
GS
n
1500 2224 M
(The interaction of several organizations produces much more complicated patterns\
, ) 5593 X
GR
0.0000 GRAY
GS
n
1000 2038 M
(however. Consider Figure 7, which shows what happens when five "median model" ) 5673 X
GR
0.0000 GRAY
GS
n
1000 1852 M
(organizations exist. As the trajectories in the "proposal display" indicate, th\
e organizational ) 6169 X
GR
0.0000 GRAY
GS
n
1000 1666 M
(positions are drawn toward the center, as their positions do not converge around\
the ) 5619 X
GR
0.0000 GRAY
GS
n
1000 1480 M
(multidimensional median, but hover near it. This arrangement produces a relativ\
ely even ) 6000 X
GR
0.0000 GRAY
GS
n
1000 1294 M
(spacing that might account for the observation that organizations inhabit "niche\
s" in the policy ) 6338 X
GR
0.0000 GRAY
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n
1000 1108 M
(space.) 412 X
GR
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7334 10600 M
(11) 166 X
GR
0.0000 GRAY
GS
n
1500 9664 M
(If the number of organizations is significantly increased, some very interesting\
and ) 5566 X
GR
0.0000 GRAY
GS
n
1000 9478 M
(complex dynamics begin to appear. In Figure 8, there are 40 organizations that \
adjust their ) 6116 X
GR
0.0000 GRAY
GS
n
1000 9292 M
(membership according to the median model. These organizations confront free-rid\
erism and ) 6234 X
GR
0.0000 GRAY
GS
n
1000 9106 M
(their members are not perfectly loyal. And, vitally, the organizations have no \
way to ) 5708 X
GR
0.0000 GRAY
GS
n
1000 8920 M
(communicate with all citizens simultaneously. Instead, they draw samples of 10,\
000 in each of ) 6393 X
GR
0.0000 GRAY
GS
n
1000 8734 M
(the first four periods, and then they are only allowed to contact existing membe\
rs and draw ) 6111 X
GR
0.0000 GRAY
GS
n
1000 8548 M
(samples from the citizen list \(sample size is eight times current membership le\
vel\). This code is ) 6454 X
GR
0.0000 GRAY
GS
n
1000 8362 M
(written so any organization with less than 10 members is removed from the simula\
tion.) 5811 X
GR
0.0000 GRAY
GS
n
1500 7990 M
(In Figure 8, there are 10 organizations remaining after 200 time periods. The p\
ositions of ) 6026 X
GR
0.0000 GRAY
GS
n
1000 7804 M
(organizations 7 and 0 are virtually "on top" of each other, but the even spacing\
about the center ) 6383 X
GR
0.0000 GRAY
GS
n
1000 7618 M
(of the policy space is unmistakable. ) 2461 X
GR
0.0000 GRAY
GS
n
1500 7246 M
(The high rate of organizational extinction in Figure 8 is a source for concern. \
Why are ) 5872 X
GR
0.0000 GRAY
GS
n
1000 7060 M
(organizations that have the ability to adjust their policies to suit their membe\
rship base more ) 6201 X
GR
0.0000 GRAY
GS
n
1000 6874 M
(likely to die out? To see they are indeed more likely to die out, consider Figu\
re 9. In Figure 9, a ) 6497 X
GR
0.0000 GRAY
GS
n
1000 6688 M
(model in which recruiter positions are fixed and organizations compete for membe\
rs is ) 5826 X
GR
0.0000 GRAY
GS
n
1000 6502 M
(presented. This figure is simply a re-run of Figure 3, except this one include\
s free-riding and ) 6296 X
GR
0.0000 GRAY
GS
n
1000 6316 M
(disloyalty. The simulation in Figure 9 is identical in every respect then, wit\
h Figure 8, except ) 6332 X
GR
0.0000 GRAY
GS
n
1000 6130 M
(that in Figure 9 organizational positions are fixed. In Figure 9, 28 of 40 organ\
izations survive for ) 6500 X
GR
0.0000 GRAY
GS
n
1000 5944 M
(200 periods, about three times as many as the median model.) 4063 X
GR
0.0000 GRAY
GS
n
1500 5572 M
(Why did so many organizations die when they were given the autonomy to govern ) 5538 X
GR
0.0000 GRAY
GS
n
1000 5386 M
(themselves and adjust their positions? When subjected to free-riding and disloy\
alty of the same ) 6433 X
GR
0.0000 GRAY
GS
n
1000 5200 M
(magnitude, organizations that are fixed in highly unpopular positions can go out\
of business, but ) 6458 X
GR
0.0000 GRAY
GS
n
1000 5014 M
(marginally placed organizations are more likely to survive. The explanation has\
two parts. First, ) 6528 X
GR
0.0000 GRAY
GS
n
1000 4828 M
(the process of "competitive exclusion" can be slow and unpredictable. Second, o\
rganizations in ) 6434 X
GR
0.0000 GRAY
GS
n
1000 4642 M
(a close vicinity tend to be drawn together by the similarity of the tastes of th\
eir members. ) 6029 X
GR
0.0000 GRAY
GS
n
1500 4270 M
(Because organizations are contacting samples from the list of citizens, there is\
no logical ) 5954 X
GR
0.0000 GRAY
GS
n
1000 4084 M
(reason why two organizations with highly similar policy stances cannot exist at \
the same time. ) 6419 X
GR
0.0000 GRAY
GS
n
1000 3898 M
(Many citizens join the "wrong organization," in the sense that they are invited \
to join by a ) 6025 X
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(recruiter and they don't know that there are other organizations that they would\
in fact prefer. ) 6319 X
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1000 3526 M
(Because those people join, they participate and draw the organization in the dir\
ection of the other ) 6523 X
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1000 3340 M
(organizations that they "should have joined" at the start. They would switch to\
the other ) 5928 X
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1000 3154 M
(organization if they are contacted by it, but until they are, they stay and try \
to make the ) 5836 X
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1000 2968 M
(organization they joined look like the organization they should have joined. As\
two ) 5632 X
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0.0000 GRAY
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1000 2782 M
(organizations are drawn together by the similarity of their members, a kind of "\
tipping effect" ) 6290 X
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0.0000 GRAY
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1000 2596 M
(occurs and one organization ends up dominating the other because it is able to c\
ontact more ) 6192 X
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0.0000 GRAY
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1000 2410 M
(prospective members. Hence, organizations grow initially because they are in a \
popular part of ) 6398 X
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0.0000 GRAY
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1000 2224 M
(the policy space, and later they are wiped out when neighbors with bigger member\
ship bases ) 6225 X
GR
0.0000 GRAY
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1000 2038 M
(\(and the ability to contact more citizens!\) develop. Notice organizations 2 o\
r 12 in Figure 8. ) 6235 X
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1500 1666 M
(In Figure 8, it is not apparent \(because the graphics are not excellent!\) tha\
t the policy ) 5746 X
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1000 1480 M
(position of organization 7 is almost exactly on top of the position organization\
0 at time 200. ) 6255 X
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1000 1294 M
(This is an example of two organizations that share a similar policy position and\
, at time 200, they ) 6531 X
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(seem to happily coexist. It doesn't stay that way for long. In Figure 10, a sna\
pshot of the model ) 6437 X
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(12) 166 X
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(at time 300 is shown and organization 0 is in the process of killing off organiz\
ation 7. As an ) 6212 X
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1000 9664 M
(organization loses members, the number of prospects that it can contact is reduc\
ed, and \(barring ) 6437 X
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1000 9478 M
(a lucky draw from the random number generator!\) its membership spirals downward\
. Keep in ) 6375 X
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1000 9292 M
(mind, the budget of each citizen is restricted to equal 1, so this finding has t\
o be qualified.) 5994 X
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1500 8920 M
(To illustrate the fact that the limited information in the model is a vital part\
of the ) 5472 X
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1000 8734 M
(explanation for the extinction of so many organizations, another set of calculat\
ions was done in ) 6390 X
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1000 8548 M
(which each organization can contact each citizen during each period. In additio\
n to making the ) 6393 X
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1000 8362 M
(simulation taking much longer, the results bear out the hypothesis. In Figure 1\
1, a model with 40 ) 6536 X
GR
0.0000 GRAY
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1000 8176 M
(organizations that are median driven is presented. Each organization's position\
is broadcasted to ) 6498 X
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0.0000 GRAY
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1000 7990 M
(all citizens in each time period. None of the organizations go out of business.\
Their policy ) 6124 X
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0.0000 GRAY
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1000 7804 M
(offerings shift a bit, but they do not exhibit the same evenly-spaced pattern as\
was seen in Figure ) 6500 X
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1000 7618 M
(8.) 125 X
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1500 7432 M
(It appears that the method in which organizations adjust their policy offerings \
may also ) 5855 X
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0.0000 GRAY
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1000 7246 M
(play a major role in our understanding of their development. If organizational \
policy offerings ) 6356 X
GR
0.0000 GRAY
GS
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1000 7060 M
(are adjusted by a recruiter in a search to increase membership, as described abo\
ve, the dynamics ) 6462 X
GR
0.0000 GRAY
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n
1000 6874 M
(in the system are quite different. Figure 12 shows what happens in a system wit\
h 40 of these ) 6238 X
GR
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1000 6688 M
(recruiters. Two major contrasts appear. First, compared to the median model, m\
ore ) 5660 X
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0.0000 GRAY
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1000 6502 M
(organizations survive through 200 periods \(24 compared against 10\). Second, t\
he policy ) 5952 X
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0.0000 GRAY
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1000 6316 M
(positions of the organizations are not arranged in an even symmetrical pattern a\
bout the center. ) 6416 X
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0.0000 GRAY
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1000 6130 M
(Rather, there are organizations with very similar positions.) 3909 X
GR
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1500 5758 M
(The conjecture that the interest group universe is populated with interest organ\
izations ) 5797 X
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1000 5572 M
(that occupy niches which separate them from each other can be supported under a \
limited set of ) 6417 X
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1000 5386 M
(conditions. It appears that conjectures about the representativeness of the pol\
itical universe will ) 6441 X
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1000 5200 M
(depend on individual-level assumptions made about recruiters and citizens.) 5007 X
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3852 4828 M
(Work that Remains) 1295 X
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1500 4456 M
(The findings here are presented as a snapshot of the development of this researc\
h project. ) 6040 X
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1000 4270 M
(I've made quite a bit of progress in the Swarm tookit and Objective-C coding. T\
he model as ) 6224 X
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1000 4084 M
(currently designed gives promise that some important findings may await.) 4940 X
GR
0.0000 GRAY
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1500 3712 M
(The models investigated here illustrate the importance of the problem faced by i\
nterest ) 5824 X
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1000 3526 M
(group leaders. Organizations must search for members in a sea of citizens, and \
in the process of ) 6464 X
GR
0.0000 GRAY
GS
n
1000 3340 M
(searching, they can never be quite sure what they will find. Organizations that\
are fixed at ) 6073 X
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0.0000 GRAY
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1000 3154 M
(positions on the extreme of the policy spectrum find fewer members, and must rec\
ruit at a higher ) 6508 X
GR
0.0000 GRAY
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1000 2968 M
(volume than organizations at positions closer to the center. When organizationa\
l positions can ) 6347 X
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1000 2782 M
(adjust, there are complex patterns of interaction that occur among the competing\
organizations.) 6345 X
GR
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1500 2410 M
(When organizations are governed by their median member, their positions tend to \
drift ) 5819 X
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1000 2224 M
(into the center of the policy spectrum and many organizations are put out of bus\
iness because ) 6272 X
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0.0000 GRAY
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1000 2038 M
(they are unable to contact prospects at a sufficiently high rate. The long run \
for such an ) 5899 X
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0.0000 GRAY
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1000 1852 M
(environment seems to hold best possibility for an outcome consistent with the id\
ea of niches in ) 6363 X
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1000 1666 M
(the political space that separate organizations. On the other hand, if organiza\
tions are created at ) 6438 X
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1000 1480 M
(random and fixed in place, or if their organizers can search in the space to inc\
rease their ) 5906 X
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(13) 166 X
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(membership base, then the outcomes are more complicated. More organizations sur\
vive under ) 6405 X
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1000 9664 M
(those conditions and the positions they adopt do not seem to follow any pleasant\
spacing pattern.) 6494 X
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1500 9292 M
(At the current time, there are four major priorities in this research project. \
First, once ) 5747 X
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1000 9106 M
(they are isolated, interesting results need to be subjected to a high number of \
repetitions in order ) 6457 X
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1000 8920 M
(to ascertain their generality. I believe it is true that organizations governed\
by their median are ) 6374 X
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1000 8734 M
(more likely to die off over time than other organizations, for example, but more\
than a few ) 6086 X
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1000 8548 M
(simulation runs will be required to be convincing. Repeating an experiment is o\
ne of the things ) 6418 X
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1000 8362 M
(that is not so easy with the Swarm toolkit and some additional effort is require\
d in coding. ) 6092 X
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1500 7990 M
(Second, it is important to understand the implications of political interaction \
in a policy ) 5866 X
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1000 7804 M
(space of extremely high dimensionality. Suppose there are 100 dimensions to pub\
lic policy. The ) 6540 X
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1000 7618 M
(model as currently written can easily handle such a conjecture. However, there \
is one problem. ) 6450 X
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1000 7432 M
(Each organization has to take a position on each of the issues, and each citizen\
has to have an ) 6262 X
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1000 7246 M
(opinion about each issue. It is possible to put a weight of 0 on an issue to re\
present the ) 5842 X
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1000 7060 M
(possibility that it is ignored. However, it is not so easy to specify the model\
in which recruiters ) 6397 X
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1000 6874 M
(are allowed to ignore some issues. It is not clear how to model a citizen's mem\
bership decision ) 6428 X
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1000 6688 M
(when an issue is ignored by an organization.) 2959 X
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1500 6316 M
(Third, additional care needs to be put into incorporating the possibility that o\
rganizations ) 5971 X
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1000 6130 M
(are added as time goes by. The code as currently constructed easily accommodate\
s such birth ) 6298 X
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1000 5944 M
(events, thanks to the object-oriented nature of Swarm and the liberal usage of l\
inked-lists. I have) 6477 X
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1000 5758 M
(in fact run simulations in which organizations are added at random according to \
a schedule. I'm ) 6510 X
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1000 5572 M
(looking for a more persuasive design than simply adding organizations every 10th\
time period or ) 6498 X
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1000 5386 M
(so.) 190 X
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1500 5014 M
(Fourth, the budget that controls the ability of citizens to join organizations h\
as to be ) 5621 X
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1000 4828 M
(investigated. If the budget is 2 or 3, a person need not choose between organiz\
ations, and ) 6022 X
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1000 4642 M
(perhaps the selective pressure of competitive position taking will be weakened. \
The code is ) 6169 X
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1000 4456 M
(currently designed to investigate this problem. I'm interested to find out if \
increasing the budget ) 6531 X
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1000 4270 M
(of citizens with certain kinds of policy preferences will cause a significant ch\
ange in the ) 5915 X
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1000 4084 M
(positions offered by \(and membership patterns in\) organized interests.) 4669 X
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3879 3340 M
(References) 741 X
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1000 2782 M
(Andreoni, James. 1988. Privately Provided Public Goods in a Large Economy: The L\
imits of ) 6252 X
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(Altruism. ) 676 X
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(Journal of Public Economics) 1936 X
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(, 35: 57-73.) 768 X
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(Axelrod, Robert. 1997. ) 1582 X
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(The Complexity of Cooperation) 2102 X
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(. Princeton, NJ: Princeton University ) 2503 X
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(Press.) 395 X
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(Axelrod, Robert. 1984. ) 1582 X
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(. New York: Basic Books.) 1749 X
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(Epstein, Joshua M. and Robert Axtell. 1996. ) 3002 X
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(Growing Artificial Societies: Social Science from ) 3327 X
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(the Bottom Up.) 1012 X
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2012 1108 M
( Washington,DC: Brookings Institution Press.) 3061 X
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1000 9478 M
(Policy Formation in a Democratic Society. ) 2892 X
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(In J.L. Bernd, ed. ) 1206 X
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( Dallas, TX: Southern Methodist University Press.) 3359 X
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(Davis, Otto A., Melvin J. Hinch and Peter C. Ordeshook. 1970. An Expository Deve\
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(Mathematical Model of the Political Process. ) 3049 X
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(American Political Science Review) 2334 X
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( 64: 426-448.) 892 X
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(DeAngelis, D.L and L.J. Gross, eds. 1992. ) 2871 X
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(Individual-based Models and Approaches in Ecology: ) 3643 X
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(Populations, Communities, and Ecosystems) 2907 X
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(. Chapman & Hall.) 1264 X
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(Forrest, Stephanie and Terry Jones. 1995. Modeling Complex Adaptive Systems with\
Echo. In ) 6353 X
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(R.J. Stonier and X.H. Yu, eds., ) 2108 X
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(Press, pp. 3-21.) 1034 X
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(Lobbying Communities in the American States) 3091 X
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(. Ann Arbor: University of Michigan Press.) 2939 X
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(Gray, Virginia, and David Lowery. 1996b. A Niche Theory of Interest Representat\
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(Journal ) 559 X
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(Holland, John H. 1998. ) 1595 X
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(. Reading, Mass.: Helix Books.) 2082 X
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(Huston, Michael, Donald DeAngelis, and Wilfred Post. 1988. New Computer Models U\
nify ) 6183 X
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(Ecological Theory. ) 1318 X
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1000 5220 M
(Johnson, Paul E. 1998. Rational Actors versus Adaptive Agents: Social Science Im\
plications. ) 6272 X
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1000 5034 M
(Paper presented at the annual meeting of the American Political Science Associat\
ion, Boston, ) 6277 X
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1000 4848 M
(Mass, Sept. 2-6.) 1080 X
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(Johnson, Paul E. 1996. Unraveling in a Variety of Institutional Settings. ) 4875 X
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(Jones, Terry, Peter T. Hraber, and Stephanie Forrest. 1996. The Ecology of Echo.\
Santa Fe ) 6105 X
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(Institute, Unpublished Ms.) 1775 X
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(Kauffman, Stuart A. 1988. The Evolution of Economic Webs. In Philip W. Anderson,\
Kenneth J. ) 6525 X
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(Arrow, and David Pines, eds. ) 2000 X
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1000 1892 M
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(461-490.) 596 X
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(Langton, Christopher G., ed. 1989. ) 2369 X
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(on the Synthesis and Simulation of Living Systems.) 3373 X
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(Olson, Mancur, Jr. 1965. ) 1746 X
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