Agent Street: An Environment for Exploring Agent-Based Models in Second Life

Urban models can be seen on a continuum between iconic and symbolic. Generally speaking, iconic models are physical versions of the real world at some scaled down representation, while symbolic models represent the system in terms of the way they function replacing the physical or material system by some logical and/or mathematical formulae. Traditionally iconic and symbolic models were distinct classes of model but due to the rise of digital computing the distinction between the two is becoming blurred, with symbolic models being embedded into iconic models. However, such models tend to be single user. This paper demonstrates how 3D symbolic models in the form of agent-based simulations can be embedded into iconic models using the multi-user virtual world of Second Life. Furthermore, the paper demonstrates Second Life\'s potential for social science simulation. To demonstrate this, we first introduce Second Life and provide two exemplar models; Conway\'s Game of Life, and Schelling\'s Segregation Model which highlight how symbolic models can be viewed in an iconic environment. We then present a simple pedestrian evacuation model which merges the iconic and symbolic together and extends the model to directly incorporate avatars and agents in the same environment illustrating how \'real\' participants can influence simulation outcomes. Such examples demonstrate the potential for creating highly visual, immersive, interactive agent-based models for social scientists in multi-user real time virtual worlds. The paper concludes with some final comments on problems with representing models in current virtual worlds and future avenues of research.Agent-Based Modelling, Pedestrian Evacuation, Segregation, Virtual Worlds, Second Life

Phase Transitions Occurring in Models of Neighborhood Racial Segregation

This thesis is organized as two chapters whose contents are closely related yet quite distinct. The first chapter presents a paper Role of \u27Vision\u27 in Neighborhood Racial Segregation: A Variant of the Schelling Segregation Model, authored by myself and Dr. Jaggi, which has been accepted for publication by the journal Urban Studies and is currently in press (as of April 2003). This chapter introduces the well-known Schelling model of neighborhood segregation, outlines the sociopolitical motivation for our work, and presents the key results that we believe are of interest to social scientists. Chapter two, which ought to be of greater interest to the physics community, presents the results of our investigations into the parallels between the Schelling model and critical phenomena. Our primary extension of the Schelling model was to include social agents who can authentically \u27see\u27 their neighbors up to a distance R, called \u27vision\u27. By exploring the consequences of systematically varying R, we have developed an understanding of how vision interacts with racial preferences and minority concentrations and leads to novel, complex segregation behavior. We have discovered three regimes: an unstable regime, where societies invariably segregate; a stable regime, where integrated societies remain stable; and an intermediate regime where a complex behavior is observed. Since the primary audience of Urban Studies consists of sociologists and economists, we have not elaborated in the first chapter upon the phase transition which was strongly suggested by the complex behavior in the intermediate regime. The purpose of chapter two then, is to elucidate these additional physically interesting aspects of our model. Melting is a textbook example of first order (discontinuous) phase transitions. These are marked by two central features: a sharp temperature at which the transition occurs, and the coexistence of the two phases at that melting point. One can study the first-order phase transition that ice undergoes when melting into water by observing the ice while continuously raising its temperature. However, if you were only able to view the system at certain discrete temperatures, you would only see a either a piece of ice or a puddle of water during each observation. Thus in order to study the potential phase transition occurring in our model, we must be able to control the governing parameters continuously. However, in our original \u27discrete\u27 model, R measures how far an agent sees from its own home as an integer number of houses. Since we can only assign discrete values to R, it is meaningless to speak of a phase transition occurring as a function of this variable. To overcome the limitations of our first model, we introduce a continuous model in chapter two where the range of vision (denoted R2 for notational clarity) can be varied continuously. This model uses a utility function that assigns greater weight to neighbors nearer an evaluating agent. The function used to model this decrease in utility contribution with distance is an exponentially decaying curve. We control the steepness of this curve (and thereby control the agents\u27 vision) using R2. Since R2 can be set to equal any positive real number, we can indeed study the possible phase transition in our simulations\u27 behavior as the function of a continuous variable. Additionally, the continuous model demonstrates the robustness of the sociologically relevant conclusions drawn in chapter one. Our continuous model, a generalization of a model developed by Wasserman and Yohe (2001), is in fact more realistic than our first model. In particular, we were pleased to discover the same three behavioral regimes and all associated trends in both our discrete model and our continuous model. This confirms that our original results were robust and not merely algorithmic artifacts related to the specific treatment of vision used in our discrete model

Segregation with Social Linkages: Evaluating Schelling's Model with Networked Individuals

This paper generalizes the original Schelling (1969, 1971a,b, 2006) model of racial and residential segregation to a context of variable externalities due to social linkages. In a setting in which individuals' utility function is a convex combination of a heuristic function a la Schelling, of the distance to friends, and of the cost of moving, the prediction of the original model gets attenuated: the segregation equilibria are not the unique solutions. While the cost of distance has a monotonic pro-status-quo effect, equivalent to that of models of migration and gravity models, if friends and neighbours are formed following independent processes the location of friends in space generates an externality that reinforces the initial configuration if the distance to friends is minimal, and if the degree of each agent is high. The effect on segregation equilibria crucially depends on the role played by network externalities.Comment: 38 pages, 24 figure

Dynamic models of residential segregation: An analytical solution

We propose an analytical solution to a Schelling segregation model for a relatively broad range of utility functions. Using evolutionary game theory, we provide existence conditions for a potential function, which characterizes the global configuration of the city and is maximized in the stationary state. We use this potential function to analyze the outcome of the model for three utility functions corresponding to different degrees of preference for mixed neighborhoods: (i) we show that linear utility functions is the only case where the potential function is proportional to collective utility, the latter being therefore maximized in stationary configurations; (ii) Schelling's original utility function is shown to drive segregation at the expense of collective utility; (iii) if agents have a strict preference for mixed neighborhoods but also prefer to be in the majority versus the minority, the model converges to perfectly segregated configurations, which clearly diverge from the social optimum. Departing from the existing literature, these conclusions are based on analytical results which open the way to analysis of many preference structures. Since our model is based on bounded rather than continuous neighborhoods as in Schelling's original model, we discuss the differences generated by the bounded- and continuous-neighborhood definitions and show that, in the case of the continuous neighborhood, a potential function exists if and only if the utility functions are linear. A side result is that our analysis builds a bridge between Schelling's model and the Duncan and Duncan segregation index.

The Surprising Success of a Replication That Failed

In a recent paper (jasss.soc.surrey.ac.uk/12/4/11.html), Oliver Will contends that the effect of mobility on trust that we originally reported (2002) depends on \'an assumption that is most probably an unwilling, unintended, and unwanted implication of the code.\' When we experimented with Will\'s revised model, we came to the opposite conclusion: his version provides stronger support for our theory than does our original. The explanation is that Will left the learning rate at the upper limit of 1.0, the level we assumed in our original paper. When we lowered the learning rate to compensate for the removal of the contested assumption, the results showed how mobility can lead to an increase in trust, which is consistent with our explanation for higher trust in the US compared to Japan. Moreover, the model also shows that it is possible for there to be too much mobility.Trust, Mobility, Replication

Invisible Hand Explanations: the Case of Menger's Explanation of the 'Origin of Money'

Menger's explanation of the 'Origin of Money' is one of the paradigmatic examples of invisible hand explanations. This paper examines Menger's explanation in detail and comments on the characteristics of invisible hand explanations.invisible hand, invisible hand explanations, Menger, origin of Money, Explanation

The Logic of the Method of Agent-Based Simulation in the Social Sciences: Empirical and Intentional Adequacy of Computer Programs

The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. We demonstrate that the logic of social simulation implies at least two distinct types of program verifications that reflect an epistemological distinction in the kind of knowledge one can have about programs. Computer programs seem to possess a causal capability (Fetzer, 1999) and an intentional capability that scientific theories seem not to possess. This distinction is associated with two types of program verification, which we call empirical and intentional verification. We demonstrate, by this means, that computational phenomena are also intentional phenomena, and that such is particularly manifest in agent-based social simulation. Ascertaining the credibility of results in social simulation requires a focus on the identification of a new category of knowledge we can have about computer programs. This knowledge should be considered an outcome of an experimental exercise, albeit not empirical, acquired within a context of limited consensus. The perspective of intentional computation seems to be the only one possible to reflect the multiparadigmatic character of social science in terms of agent-based computational social science. We contribute, additionally, to the clarification of several questions that are found in the methodological perspectives of the discipline, such as the computational nature, the logic of program scalability, and the multiparadigmatic character of agent-based simulation in the social sciences.Computer and Social Sciences, Agent-Based Simulation, Intentional Computation, Program Verification, Intentional Verification, Scientific Knowledge

Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science

The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies.Agent-Based Models, Empirical Calibration and Validation, Taxanomy of Models