Information Theoretic Models of Social Interaction

This dissertation demonstrates, in a non-semantic information-theoretic framework, how the principles of \maximisation of relevant information" and \information parsimony" can guide the adaptation of an agent towards agent-agent interaction. Central to this thesis is the concept of digested information; I argue that an agent is intrinsically motivated to a.) process the relevant information in its environment and b.) display this information in its own actions. From the perspective of similar agents, who require similar information, this di erentiates other agents from the rest of the environment, by virtue of the information they provide. This provides an informational incentive to observe other agents and integrate their information into one's own decision making process. This process is formalized in the framework of information theory, which allows for a quantitative treatment of the resulting e ects, speci cally how the digested information of an agent is in uenced by several factors, such as the agent's performance and the integrated information of other agents. Two speci c phenomena based on information maximisation arise in this thesis. One is ocking behaviour similar to boids that results when agents are searching for a location in a girdworld and integrated the information in other agent's actions via Bayes' Theorem. The other is an e ect where integrating information from too many agents becomes detrimental to an agent's performance, for which several explanations are provided

Knowledge exchange, matching, and agglomeration

Despite wide recognition of their significant role in explaining sustained growth and economic development, uncompensated knowledge spillovers have not yet been fully modeled with a microeconomic foundation. The main purpose of this paper is to illustrate the exchange of knowledge as well as its consequences on agglomerative activity in a general-equilibrium search-theoretic framework. Agents, possessing differentiated types of knowledge, search for partners to exchange ideas and create new knowledge in order to improve production efficacy. When individuals’ types of knowledge are too diverse, a match is less likely to generate significant innovations. We demonstrate the extent of agglomeration has significant implications for the patterns of information flows in economies. Further, by simultaneously determining the patterns of knowledge exchange and the spatial agglomeration of an economy we identify additional channels for interaction between agglomerative activity and knowledge exchange. Finally, contrary to previous work in spatial agglomeration, our model suggests that agglomerative environments may be either under-specialized and under-populated or over-specialized and over-populated relative to the social optimum.Econometric models

Evolutionary Game Theory

Articl

Emergence of social networks via direct and indirect reciprocity

Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals' degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores ("indirect reciprocity"), which is known to play an important role in many economic interactions. In order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. "tit-for-tat") as well as indirect reciprocity (helping strangers in order to increase one's reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which are dynamic at the individual level but stable at the network level

Public Good Provision: Using an Evolutionary Game Theoretic Approach to Modeling

Public Goods Provision problem is studied using a new modeling tool which enables us to find various equilibria of the game, which are robust to any functional form specification.Public Goods, Evolution of Cooperation, Qualitative Evolutionary Games

A comparative study of game theoretic and evolutionary models for software agents

Most of the existing work in the study of bargaining behaviour uses techniques from game theory. Game theoretic models for bargaining assume that players are perfectly rational and that this rationality in common knowledge. However, the perfect rationality assumption does not hold for real-life bargaining scenarios with humans as players, since results from experimental economics show that humans find their way to the best strategy through trial and error, and not typically by means of rational deliberation. Such players are said to be boundedly rational. In playing a game against an opponent with bounded rationality, the most effective strategy of a player is not the equilibrium strategy but the one that is the best reply to the opponent's strategy. The evolutionary model provides a means for studying the bargaining behaviour of boundedly rational players. This paper provides a comprehensive comparison of the game theoretic and evolutionary approaches to bargaining by examining their assumptions, goals, and limitations. We then study the implications of these differences from the perspective of the software agent developer