Political Parties and Electoral Landscapes

Kollman, Ken; Miller, John H.; Page, Scott E.

doi:10.1017/s0007123498000131

Cited by 128 publications

(71 citation statements)

References 11 publications

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“…We find, for example, that voters with extremist preferences form electoral landscapes with more gradual slopes than do voters with centrist preferences. This agrees with our earlier computational findings that adaptive parties facing voters with extremist preferences tended to converge to moder ate platforms less quickly than parties facing voters with centrist preferences (Kollman, Miller, and Page, 1993) and with recent empirical work on platform convergence (Koll man, Miller, and Page, 1994).…”

Section: Introductionsupporting

confidence: 92%

“…The research has led to three conclusions: first, that over a series of elections adaptive parties will choose moderate platforms with some degree of separation on issues (Kollman, Miller, and Page, 1992;Page, Kollman, and Miller, 1992); second, that incum bents may win re-election because challengers a. re unable to locate winning platforms; and third, that changes in the distributions of voters' preferences may influence electoral competition and, consequently, electoral outcomes (Kollman, Miller, and Page, 1993).…”

Section: Introductionmentioning

confidence: 99%

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Landscape formation in a spatial voting model

1997

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Section: Introductionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Landscape formation in a spatial voting model

1997

Self Cite

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“…Love it, or hate it (Binmore, 1994), the simulations of the Prisoner's Dilemma in the last three decades outweigh any other topic, and by a considerable margin (see Hoffmann, 2000;Axelrod & D'Ambrosio, 1996;Gotts et al, 2003). Public opinion dynamics (to cite just a few, (Nowak et al, 1990;Latane, 1996;Huckfeldt et al, 2004)) or competitive position taking by political parties Kollman et al, 1992Kollman et al, , 1998de Marchi, 1999;Laver, 2005;Laver & Schilperoord, 2007;Fowler & Laver, 2008 have also received a considerable amount of attention.…”

Section: Simulation Modeling and Hypothesis Constructionmentioning

confidence: 99%

Monte Carlo Analysis in Academic Research

Johnson

2013

Oxford Handbooks Online

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Monte Carlo analysis is a research strategy that incorporates randomness into the design, implementation or evaluation of theoretical models. It began in the 1940s, when the development of computer hardware and mathematical models made it possible to generate streams of random numbers. These random number streams are combined with mathematical models in order to create models and evaluate theories of random processes. This chapter attempts to tame this diverse, unmanageable collection of concepts and methods by dividing simulation projects into three types. The first, commonly called "Monte Carlo simulation," is used to evaluate statistical estimators. When an estimation procedure is proposed, it is standard procedure to test it against a variety of simulated research problems. A second type of project, referred to as "Markov chain Monte Carlo" (or MCMC), helps researchers to draw conclusions about complicated probability models for which conventional research strategies do not yield insights. The third 1 type of project arises in the study of complex systems, which are characterized by a large number of loosely interconnected, autonomous elements. Commonly known as agent-based models, these simulations have found enthusiastic advocates in environmental and social sciences.Keywords: Monte Carlo, Markov chain Monte Carlo (MCMC), pseudo random number generation (PRNG), Bayesian statistics, agent-based modeling Monte Carlo (MC) analysis is a general term that refers to research that employs random numbers, usually in the form of a computer model (or simulation). Although this research began in the natural sciences, computer science, and mathematics, it is now widely applied in social science as well. This essay attempts to explain the fundamental ideas that spurred the creation of these new procedures as well as their eventual adaptation for use in social science research.This chapter is not a "how to" guide for simulation, but rather it is a "what for" or "why you might want to" guide. Some of the difficulties that arise in MC research projects are considered as well. It begins with some background information on the development of computers and algorithms for random numbers. After that, the chapter takes up applications in the evaluation of proposed statistical estimators, the practice of Bayesian statistics via computer simulation, and investigation of complex systems through agent-based models. Some conclusions about the challenges that face the field are presented, along with a conclusion. A significant part of the presentation is about the exciting developments that have occurred since 1990. Rapid improvements in hardware and software have opened up opportunities for scholars to work with models that were previously prohibited by conceptual and technical barriers. At the current time, we are able to conceptualize and implement models that were simply impossible just 10 years ago. The extremely rapid progress has been driven by a fruitful interaction of substantive researchers in the natural and social science...

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“…We assume an underlying process of individual and social learning about the perceived attractiveness of the functionings of collective agents, which is fed by information on how closely these functionings match their own preferences for consumption patterns and/or lifestyles. The support canvas, implemented as a multi-dimensional "practice space", provides the underlying spatial characterization of consumer preferences, in the tradition of the agent-based models of Kollman et al (1992Kollman et al ( , 1998 and extensions thereof (Laver 2005;Laver and Schilperoord 2007).…”

Section: Agent-based Representation Of Transition and Transformation mentioning

confidence: 99%

Modelling societal transitions with agent transformation

Schilperoord

Rotmans

Bergman

2008

Comput Math Organiz Theor

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Transition models explain long-term and large-scale processes fundamentally changing the structure of a societal system. Our concern is that most transition models are too static. Although they capture a move of focus from static equilibria to transitions between dynamic equilibria, they are still rooted in an "equilibriumist" approach. Improvement is possible with agent-based models that give attention to endogenous system processes called "transformation processes". These models can render far more dynamic pictures of societal systems in transition, and are no longer remote from descriptions in the emerging transition literature.

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Political Parties and Electoral Landscapes

Cited by 128 publications

References 11 publications

Landscape formation in a spatial voting model

Landscape formation in a spatial voting model

Monte Carlo Analysis in Academic Research

Modelling societal transitions with agent transformation

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