Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics

Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics

Yu Zhang (Trinity University, USA), Mark Lewis (Trinity University, USA), Christine Drennon (Trinity University, USA), Michael Pellon (Trinity University, USA) and Coleman (Trinity University, USA)
DOI: 10.4018/978-1-60566-236-7.ch008
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Abstract

Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at least in part, by the perceived importance of fostering human-like adjustable autonomy and behaviors in social systems. But, efficient scalable and robust social systems are difficult to engineer. One difficulty exists in the design of how society and agents evolve and the other diffi- culties exist in how to capture the highly cognitive decision-making process that sometimes follows intuition and bounded rationality. We present a multi-agent architecture called CASE (Cognitive Agents for Social Environments). CASE provides a way to embed agent interactions in a three-dimensional social structure. It also presents a computational model for an individual agent’s intuitive and deliberative decision-making process. This chapter also presents our work on creating a multi-agent simulation which can help social and economic scientists use CASE agents to perform their tests. Finally, we test the system in an urban dynamic problem. Our experiment results suggest that intuitive decision-making allows the quick convergence of social strategies, and embedding agent interactions in a three-dimensional social structure speeds up this convergence as well as maintains the system’s stability.
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Introduction

In social environments, people interact with each other and form different societies (or organizations or groups). To better understand people’s social interactions, researchers have increasingly relied on computational models [16, 40, 41, 42]. A good computational model that takes into consideration both the individual and social behaviors could serve as a viable tool to help researchers analyze or predict the complex phenomena that emerge from the interactions of massive autonomous agents, especially for the domain that often requires a long time to evolve or requires exposing real people to a dangerous environment. However, efficient, scalable, and robust social systems are difficult to engineer [3].

One difficulty exists in modeling the system by holding both the societal view and the individual agent view. The societal view involves the careful design of agent-to-agent interactions so that an individual agent’s choices influence and are influenced by the choices made by others within the society. The agent view involves modeling only an individual agent’s decision-making processes that sometimes follow intuition and bounded rationality [29]. Previous research in modeling theory of agents and society in a computational framework has taken singly a point of view of society or agent. While the single societal view mainly concentrates on the centralist, static approach to organizational design and specification of social structures and thus limits system dynamics [12, 16, 35], on the other hand, the single agent view focuses on modeling the nested beliefs of the other agents, but this suffers from an explosion in computational complexity as the number of agents in the system grows.

Another difficulty in modeling theory of agent and society exists in quantitative or qualitative modeling of uncertainty and preference. In the case of quantitative modeling, the traditional models like game theory and decision theory have their own limitations. Game theory typically relies on concepts of equilibria that people rarely achieve in an unstructured social setting, and decision theory typically relies on assumptions of rationality that people constantly violate [27]. In the case of qualitative modeling, there are three basic models: prescriptive, normative and descriptive [31, 37]. A prescriptive model is one which can and should be used by a real decision maker. A normative model requires the decision maker to have perfect rationality, for example, the classical utility function belongs to this category. Many normative theories have been refined over time to better “describe” how humans make decisions. Kahneman and Tversky’s Prospect Theory [18, 34] and von Neuman and Morgenstein’s Subjective Utility Theory [36] are noted examples of normative theories that have taken on a more descriptive guise. One of the central themes of the descriptive model is the idea of Bounded Rationality [29], i.e., humans don’t calculate the utility value for every outcome; instead we use intuition and heuristics to determine if one situation is better than another. However, existing descriptive methods are mostly informal, therefore there is a growing need to study them in a systematic way and provide a qualitative framework in which to compare various possible underlying mechanisms.

Motivated by these observations, we have developed a cognitive agent model called CASE (Cognitive Agent in Social Environment). CASE is designed to achieve two goals. First, it aims to model the “meso-view” of multi-agent interaction by capturing both the societal view and the agent view. On one hand, we keep an individual perspective on the system assumed by the traditional multi-agent models, i.e. an agent is an autonomous entity and has its own goals and beliefs in the environment [5, 43]. On the other hand, we take into account how agent’s decisions are influenced by the choices made by others. This is achieved by embedding agents’ interactions in three social structures: group, which represents social connections, neighborhood, which represents space connections and network, which span social and space categories. These three structures reproduce the way information and social strategy is passed and therefore the way people influence each other. In our view, social structures are external to individual agent and independent from their goals. However, they constrain the individual’s commitment to goals and choices and contribute to the stability, predictability and manageability of the system as a whole.

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Table of Contents
Foreword
Georgi Stojanov
Chapter 1
R. Keith Sawyer
Sociology should be the foundational science of social emergence. But to date, sociologists have neglected emergence, and studies of emergence are... Sample PDF
The Science of Social Emergence
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Chapter 2
Christopher Goldspink, Robert Kay
This chapter critically examines our theoretical understanding of the dialectical relationship between emergent social structures and agent... Sample PDF
Agent Cognitive Capabilities and Orders of Social Emergence
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Chapter 3
Joseph C. Bullington
Social interaction represents a powerful new locus of research in the quest to build more truly human-like artificial agents. The work in this area... Sample PDF
Agents and Social Interaction: Insights from Social Psychology
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Chapter 4
M. Afzal Upal
This chapter will critically review existing approaches to the modeling transmission of cultural information and advocate a new approach based on a... Sample PDF
Predictive Models of Cultural Information Transmission
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Chapter 5
Jorge A. Romero
Despite the popularity of agents for the information technology infrastructure, questions remain because it is not clear what do e-business agents... Sample PDF
Interaction of Agent in E-Business: A Look at Different Sources
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Chapter 6
Adam J. Conover
This chapter presents a description of ongoing experimental research into the emergent properties of multi-agent communication in “temporally... Sample PDF
A Simulation of Temporally Variant Agent Interaction via Passive Inquiry
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Chapter 7
Richard Schilling
This chapter presents a generalized messaging infrastructure that can be used for distributed agent systems. The principle of agent feedback... Sample PDF
Agent Feedback Messaging: A Messaging Infrastructure for Distributed Message Delivery
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Chapter 8
Yu Zhang, Mark Lewis, Christine Drennon, Michael Pellon, Coleman
Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at... Sample PDF
Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics
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Chapter 9
Scott Watson, Kerstin Dautenhahn, Wan Ching (Steve) Ho, Rafal Dawidowicz
This chapter discusses certain issues in the development of Virtual Learning Environments (VLEs) populated by autonomous social agents, with... Sample PDF
Developing Relationships Between Autonomous Agents: Promoting Pro-Social Behaviour Through Virtual Learning Environments Part I
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Chapter 10
Martin Takác
In this chapter, we focus on the issue of understanding in various types of agents. Our main goal is to build up notions of meanings and... Sample PDF
Construction of Meanings in Biological and Artificial Agents
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Chapter 11
Myriam Abramson
In heterogeneous multi-agent systems, where human and non-human agents coexist, intelligent proxy agents can help smooth out fundamental... Sample PDF
Training Coordination Proxy Agents Using Reinforcement Learning
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Chapter 12
Deborah V. Duong
The first intelligent agent social model, in 1991, used tags with emergent meaning to simulate the emergence of institutions based on the principles... Sample PDF
The Generative Power of Signs: The Importance of the Autonomous Perception of Tags to the Strong Emergence of Institutions
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Chapter 13
Josefina Sierra, Josefina Santibáñez
This chapter addresses the problem of the acquisition of the syntax of propositional logic. An approach based on general purpose cognitive... Sample PDF
Propositional Logic Syntax Acquisition Using Induction and Self-Organisation
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Chapter 14
Giovanni Vincenti, James Braman
Emotions influence our everyday lives, guiding and misguiding us. They lead us to happiness and love, but also to irrational acts. Artificial... Sample PDF
Hybrid Emotionally Aware Mediated Multiagency
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Chapter 15
Samuel G. Collins, Goran Trajkovski
In this chapter, we give an overview of the results of a Human-Robot Interaction experiment, in a near zerocontext environment. We stimulate the... Sample PDF
Mapping Hybrid Agencies Through Multiagent Systems
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Chapter 16
Scott Watson, Kerstin Dautenhahn, Wan Ching (Steve) Ho, Rafal Dawidowicz
This chapter is a continuation from Part I, which has described contemporary psychological descriptions of bullying in primary schools and two... Sample PDF
Developing Relationships Between Autonomous Agents: Promoting Pro-Social Behaviour Through Virtual Learning Environments Part II
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Chapter 17
Mario Paolucci, Rosaria Conte
This chapter is focused on social reputation as a fundamental mechanism in the diffusion and possibly evolution of socially desirable behaviour... Sample PDF
Reputation: Social Transmission for Partner Selection
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Chapter 18
Adam J. Conover
This chapter concludes a two part series which examines the emergent properties of multi-agent communication in “temporally asynchronous”... Sample PDF
A Simulation of Temporally Variant Agent Interaction via Belief Promulgation
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Chapter 19
David B. Newlin
Following the discovery in Rhesus monkeys of “mirror neurons” that fire during both execution and observation of motor behavior, human studies have... Sample PDF
The Human Mirror Neuron System
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Chapter 20
Eric Baumer, Bill Tomlinson
This chapter presents an argument that the process of emergence is the converse of the process of abstraction. Emergence involves complex behavior... Sample PDF
Relationships Between the Processes of Emergence and Abstraction in Societies
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Chapter 21
Vern R. Walker
In modern legal systems, a large number of autonomous agents can achieve reasonably fair and accurate decisions in tens of thousands of legal cases.... Sample PDF
Emergent Reasoning Structures in Law
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Chapter 22
Theodor Richardson
Network Intrusion Detection Systems (NIDS) are designed to differentiate malicious traffic, from normal traf- fic, on a network system to detect the... Sample PDF
Agents in Security: A Look at the Use of Agents in Host-Based Monitoring and Protection and Network Intrusion Detection
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Chapter 23
Michael J. North, Thomas R. Howe, Nick Collier, Eric Tatara, Jonathan Ozik, Charles Macal
Search has been recognized as an important technology for a wide range of software applications. Agentbased modelers often face search challenges... Sample PDF
Search as a Tool for Emergence
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About the Contributors