Bruce Edmonds

Sociology and simulation: statistical and qualitative cross-validation

Agent‐based simulation modeling enables the construction of formal models that simultaneously can be microvalidated against accounts of individual behavior and macrovalidated against aggregate data that show the characteristics of many socially derived time series. These characteristics (leptokurtosis and clustered volatility) have two important consequences: first, they also appear in suitably structured agent‐based models where, like real social actors, agents are socially embedded and metastable; second, their presence precludes the use of many standard statistical techniques like the chi‐square test. These characteristics in time‐series data indicate that a suitable agent‐based model rather than a standard statistical model will be appropriate. This is illustrated with an agent‐based model of mutual social influence on domestic water demand. The consequences for many frequently used statistical techniques are discussed.

From KISS to KIDS: an 'anti-simplistic' modelling approach

A new approach is suggested under the slogan “Keep it Descriptive Stupid” (KIDS) that encapsulates a trend in increasingly descriptive agent-based social simulation. The KIDS approach entails one starts with the simulation model that relates to the target phenomena in the most straight-forward way possible, taking into account the widest possible range of evidence, including anecdotal accounts and expert opinion. Simplification is only applied if and when the model and evidence justify this. This contrasts sharply with the KISS approach where one starts with the simplest possible model and only moves to a more complex one if forced to. An example multi-agent simulation of domestic water demand and social influence is described.

SDML: A Multi-Agent Language for Organizational Modelling

A programming language which is optimized for modelling multi-agent interaction within articulated social structures such as organizations is described with several examples of its functionality. The language is SDML, a strictly declarative modelling language which has object-oriented features and corresponds to a fragment of strongly grounded autoepistemic logic. The virtues of SDML include the ease of building complex models and the facility for representing agents flexibly as models of cognition as well as modularity and code reusability. Two representations of cognitive agents within organizational structures are reported and a Soar-to-SDML compiler is described. One of the agent representations is a declarative implementation of a Soar agent taken from the Radar-Soar model of Ye and Carley (1995). The Ye-Carley results are replicated but the declarative SDML implementation is shown to be much less computationally expensive than the more procedural Soar implementation. As a result, it appears that SDML supports more elaborate representations of agent cognition together with more detailed articulation of organizational structure than we have seen in computational organization theory. Moreover, by representing Soar-cognitive agents declaratively within SDML, that implementation of the Ye-Carley specification is necessarily consistent and sound with respect to the formal logic to which SDML corresponds.

Socially Intelligent Agents

This introduction explains the motivation to edit this book and provides an overview of the chapters included in this book. Main themes and common threads that can be found across different chapters are identified that might help the reader in navigating the book.

A conversational intelligent tutoring system to automatically predict learning styles

This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a students learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman, 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscars tutoring helpful and achieved an average learning gain of 13%.

Sociology and Social Theory in Agent Based Social Simulation: A Symposium

A lengthy and intensive debate about the role of sociology in agent based social simulation dominated the email list simsoc@jiscmail.ac.uk during the autumn of 2000. The debate turned on the importance of models being devised to capture the properties of whole social systems and whether those properties should determine agent behaviour or, conversely, whether the properties of social systems should emerge from the behaviour and interaction of the agents and, if so, how that emergence should be represented. The positions of four of the main protagonists concerned specifically with the modelling issues are reprised and extended in this symposium.

The Use of Models - Making MABS More Informative

The use of MABS (Multi-Agent Based Simulations) is analysed as the modelling of distributed (usually social) systems using MAS (Multi-Agent Systems) as the model structure. It is argued that rarely is direct modelling of target systems attempted but rather an abstraction of the target systems is modelled and insights gained about the abstraction then applied back to the target systems. The MABS modelling process is divided into six steps: abstraction, design, inference, analysis, interpretation and application. Some types of MABS papers are characterised in terms of the steps they focus on and some criteria for good MABS formulated in terms of the soundness with which the steps are established. Finally some practical proposals that might improve the informativeness of the field are suggested.

The Insufficiency of Formal Design Methods " The Necessity of an Experimental Approach - for the Understanding and Control of Complex MAS

We highlight the limitations of formal methods by exhibiting two results in recursive function theory: that there is no effective means of finding a program that satisfies a given formal specification; or checking that a program meets a specification. We exhibit a simple MAS which has all the power of a Turing machine. We argue that any pure design methodology will face insurmountable difficulties in todayýs open and complex MAS. We recommend instead a methodology based on experimental method ¿ scientific foundations for MAS construction and control.

Social Simulation: Technologies, Advances and New Discoveries

The simulation of social behavior in a variety of domains is an increasingly important technological tool. eference survey of social simulation work, Social Simulation: Technologies, Advances and New Discoveries comprehensively collects the most exciting developments in the field. Drawing research contributions from a vibrant community of experts on social simulation, this Premier Reference Source provides a set of unique and innovative approaches, ranging from agent-based modeling to empirically based simulations, as well as applications in business, governmental, scientific, and other contexts. This book will be a significant reference tool for researchers, educators, and practitioners in such fields as sociology, geography, economics, environmental science, artificial intelligence, machine learning, computer engineering, and networks, and a valuable, interdisciplinary addition to academic libraries.

Modelling Bounded Rationality in Agent-Based Simulations Using the Evolution of Mental Models

There are many possible ways of modelling economic agents. These traditionally fall into one of two camps, dating from Simon’s distinction between substantive and procedural rationality: this is often characterised as those with bounded rationality and those with no such bounds (although this is not strictly correct, Moss & Sent forthcoming). Although the latter type is more analytically tractable we are interested in the former type.