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This paper presents an agent-based model explaining voter knowledge in the context of electoral competition. It shows that a set of simple behavioral rules implemented by voters, parties and media outlets generates novel (and testable) predictions regarding the mass-mediated underpinnings of aggregated voter knowledge and party representativeness. More specifically, it finds that increasing competition among media outlets has a positive effect on the political knowledge of the electorate at large. It also finds that increasing media competition leads to parties that are more accountable to the median voter, but only when voters care about the quality of the news alone.

Abstract:Public reason liberalism includes an ideal of political stability where justified institutions reach a kind of self-enforcing equilibrium. Such an order must be stable for the right reasons — where persons comply with the rules of the order for moral reasons, rather than out of fear or self-interest. John Rawls called a society stable in this way well-ordered.In this essay, I contend that a more sophisticated model of a well-ordered society, specifically an agent-based model, yields a richer and more attractive understanding of political stability. An agent-based model helps us to distinguish between three concepts of political stability — durability, balance, and immunity. A well-ordered society is one that possesses a high degree of social trust and cooperative behavior among its citizens (durability) with low short-run variability (balance). A well-ordered society also resists destabilization caused by noncompliant agents in or entering the system (immunity).Distinguishing between these three concepts complicates the necessary reformulation of the idea of a well-ordered society. Going forward, public reason theorists must now distinguish between types of assurance, specify heretofore unknown aspects of reasonable behavior, and reconceive of the nonideal preconditions for forming a stable, ideal social order.

This paper proposes and tests an agent-based model of worker and job matching. The model takes residential locations of workers and the locations of employers as exogenous and deals specifically with the interactions between firms and workers in creating a job-worker match and the commute outcomes. It is meant to illustrate that by explicitly modeling the search and hiring process, origins and destinations (ODs) can be linked at a disaggregate level. The model is tested on a toy-city as well as using data from the Twin Cities area. The toy-city model illustrates that the model predicts reasonable commute outcomes, with agents selecting the closest work place when wage and skill differentiation is absent in the labor market. The introduction of wage dispersion and skill differentiation in the model increases the the average home to work distances considerably. Using data from Twin Cities area of Minneapolis-St. Paul, aggregate commute and wage outcomes from the model are shown to capture the trends in the observed data. Overall, the results suggest that the behavior rules as implemented lead to reasonable patterns. Future directions are also discussed.

This paper investigates how the inclusion of political lifecycles and unrestricted housing development by private developers will impact the spatial arrangement and density of slums in a virtual urban environment. To do this, I build on the agent based model (ABM) entitled "Slumulation" developed by Crooks, Koizumi and Patel (2012). The intention of this is to generate conversation around the ways individual action impact the urban en- vironment, and also how other stakeholders in the city create conditions that motivate the emergence of certain spatial arrangements over time. Through the addition of code into the original model, I am able to augment the actions of two actors in particular: politicians and developers. Borrowing from literature, I include local political cycles that minimize the interaction between urban dwellers and politicians throughout most of the simulation, except for in the case of election times where special consideration is made that allows for lower rents and lax rule enforcement in exchange for political support. In the center of this city, housing developers are programmed to build housing for high- and middle-income households because the real estate sector and government policies are encouraging the construction of a new and modern urban image that slowly prices out lower-income residents of the inner city. These additions show that local politics and de- velopment without efforts to mitigate the impact on individual households may contribute to slums, high density urban neighborhoods, and the peripheralization of the city's most vulnerable.

Artificial economics aims to provide a generative approach to understanding problems in economics and social sciences. It is based on the consistent use of agent-based models and computational techniques. It encompasses a rich variety of techniques that generalize numerical analysis, mathematical programming, and micro-simulations. The peer-reviewed contributions in this volume address applications of artificial economics to markets and trading, auctions, networks, management, industry sectors, macroeconomics, and demographics and culture.

The paper discusses development of the investments block in an agent-based interregional multisector model of the Russian economy. The ability of the model to reproduce stationary states of the economy and economic growth under various regimes of intra-company fixed capital investment is considered. A given steady state can be achieved by choosing the shares of profits assigned to investment. Output growth was insured by reducing labor intensity by 2 % per year in the Leontief-type production functions of firms with a fixed level of labor resources. When investments are financed from the profits of firms under fixed share regime, the model economy demonstrate a balanced growth.

"Tobacco consumption continues to be the leading cause of preventable disease and death in the United States. The Food and Drug Administration (FDA) regulates the manufacture, distribution, and marketing of tobacco products - specifically cigarettes, cigarette tobacco, roll-your-own tobacco, and smokeless tobacco - to protect public health and reduce tobacco use in the United States. Given the strong social component inherent to tobacco use onset, cessation, and relapse, and given the heterogeneity of those social interactions, agent-based models have the potential to be an essential tool in assessing the effects of policies to control tobacco. Assessing the Use of Agent-Based Models for Tobacco Regulation describes the complex tobacco environment; discusses the usefulness of agent-based models to inform tobacco policy and regulation; presents an evaluation framework for policy-relevant agent-based models; examines the role and type of data needed to develop agent-based models for tobacco regulation; provides an assessment of the agent-based model developed for FDA; and offers strategies for using agent-based models to inform decision making in the future."--Publisher's description

AbstractAlthough individual behavior plays a major role in community flood risk, traditional flood risk models generally do not capture information on how community policies and individual decisions impact the evolution of flood risk over time. The purpose of this study is to improve the understanding of the temporal aspects of flood risk through a combined analysis of the behavioral, engineering, and physical hazard aspects of flood risk. Additionally, the study aims to develop a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions. An agent‐based model (ABM) is used to analyze the influence of flood protection measures, individual behavior, and the occurrence of floods and near‐miss flood events on community flood risk. The ABM focuses on the following decisions and behaviors: dissemination of flood management information, installation of community flood protection, elevation of household mechanical equipment, and elevation of homes. The approach is place based, with a case study area in Fargo, North Dakota, but is focused on generalizable insights. Generally, community mitigation results in reduced future damage, and individual action, including mitigation and movement into and out of high‐risk areas, can have a significant influence on community flood risk. The results of this study provide useful insights into the interplay between individual and community actions and how it affects the evolution of flood risk. This study lends insight into priorities for future work, including the development of more in‐depth behavioral and decision rules at the individual and community level.

We present an exhaustive description of a stochastic, event-driven, hierarchical agent-based model designed to reproduce the infectious state of the cattle disease called Bovine Viral Diarrhea, for which the livestock-trade network is the main route of spreading. For the farm-node dynamics, it takes into account a vast number of breeding, infectious and animal movement mechanisms via a susceptible-infected-recovered type of dynamics with an additional permanently infectious class. The interaction between the farms is described by a supply-demand farm manager mechanism governing the network structure and dynamics. The model includes realistic disease and breeding dynamics and allows to study numerous mitigation strategies of present and past government regulations, including different testing and vaccination scenarios.