Interacting agents in finance, entry written for the New Palgrave Dictionary of Economics, Second Edition, edited by L. Blume and S. Durlauf, Palgrave Macmillan, forthcoming 2006.

Interacting agents in finance represent a behavioral, agent-based approach in which financial markets are viewed as complex adaptive systems consisting of many boundedly rational agents interacting through simple heterogeneous investment strategies, constantly adapting their behavior in response to new information, strategy performance and through social interactions. An interacting agent system acts as a noise filter, transforming and amplifying purely random news about economic fundamentals into an aggregate market outcome exhibiting important stylized facts such as unpredictable asset prices and returns, excess volatility, temporary bubbles and sudden crashes, large and persistent trading volume, clustered volatility and long memory.

Human Development Dynamics: An Agent Based Simulation of Macro Social Systems and Individual Heterogeneous Evolutionary Games

Purpose: In the context of modernization and development, a complex adaptive systems framework can help address the coupling of macro social constraint and opportunity with individual agency. Combining system dynamics and agent based modeling, we formalize a simulation approach of the Human Development (HD) perspective to explore the interactive effects of economics, culture, society and politics across multiple human scales. Methods: Based on a system of asymmetric, coupled nonlinear equations, we first capture the core qualitative logic of HD theory, empirically validated from World Values Survey (WVS) data. Using a simple evolutionary game approach, second we fuse endogenously derived individual socio-economic attribute changes with Prisoner’s Dilemma in an agent based model of the interactive political-cultural effects of heterogeneous, spatial intra-societal economic transactions. We then explore a new human development dynamics (HDD) model behavior via quasiglobal simulation methods to identify paths and pitfalls towards economic development, cultural plasticity, social and political change behavior. Results: Our preliminary results suggest strong nonlinear path dependence and complexity in three areas: adaptive development processes, co-evolutionary societal transactions and near equilibrium development trajectories, with significant implications for anticipating and managing positive development outcomes. Strong local epistatic interactions characterized by adaptive co-evolution, shape higher order global conditions and ultimately societal outcomes. Conclusions: Techno-social simulations such as this can provide scholars and policymakers alike insights into the nonlinear, complex adaptive effects of societal co-evolution. We believe complex adaptive or evolutionary systems approaches are necessary to understand both near and potentially catastrophic, far-from-equilibrium behavior and societal outcomes across all human scales of modernization

Multi-method Modeling Framework for Support of Integrated Water Resources Management

The existing definition of integrated water resources management (IWRM) promotes a holistic approach to water resources management practice. The IWRM deals with planning, design and operation of complex systems in order to control the quantity, quality, temporal and spatial distribution of water with the main objective of meeting human and ecological needs and providing protection from water disasters. One of the main challenges of IWRM is development of tools for operational implementation of the concept and dynamic coupling of physical and socio-economic components of water resources systems. This research examines the role of simulation in IWRM practices, analyses the advantages and limitations of existing modeling methods, and, as a result, suggests a new generic multi-method modeling framework that has the main goal to capture all structural complexities and interactions within water resources systems. Since traditional modeling methods solely do not provide sufficient support, this framework uses multi-method simulation approach to examine the co-dependence between natural resources and socio-economic environment. Designed framework consists of (i) a spatial database, (ii) a process-based model for representing the physical environment and changing conditions, and (iii) an agent-based model for representing spatially explicit socio-economic environment. The main idea behind multi-agent models is to build virtual complex systems composed of autonomous entities, which operate on local knowledge, possess limited abilities, affect and are affected by local environment, and thus enact the desired global system behavior. Based on the architecture of the generic multi-method modeling framework, an operational model is developed for the Upper Thames River basin, Southwestern Ontario, Canada. Six different experiments combine three climate and two socio-economic scenarios to analyze spatial dynamics of a complex physical-social-economic system. Obtained results present strong dependence between changes in hydrologic regime, in this case surface runoff and groundwater recharge rates, and regional socio-economic activities

Human Development Dynamics: an Agent Based Simulation of Macro Social Systems and Individual Heterogeneous Evolutionary Games

This is the final version of the article. Available from Springer via the DOI in this record.Purpose In the context of modernization and development, a complex adaptive systems framework can help address the coupling of macro social constraint and opportunity with individual agency. Combining system dynamics and agent based modeling, we formalize a simulation approach of the Human Development (HD) perspective to explore the interactive effects of economics, culture, society and politics across multiple human scales. Methods Based on a system of asymmetric, coupled nonlinear equations, we first capture the core qualitative logic of HD theory, empirically validated from World Values Survey (WVS) data. Using a simple evolutionary game approach, second we fuse endogenously derived individual socio-economic attribute changes with Prisoner’s Dilemma in an agent based model of the interactive political-cultural effects of heterogeneous, spatial intra-societal economic transactions. We then explore a new human development dynamics (HDD) model behavior via quasi-global simulation methods to identify paths and pitfalls towards economic development, cultural plasticity, social and political change behavior. Results Our preliminary results suggest strong nonlinear path dependence and complexity in three areas: adaptive development processes, co-evolutionary societal transactions and near equilibrium development trajectories, with significant implications for anticipating and managing positive development outcomes. Strong local epistatic interactions characterized by adaptive co-evolution, shape higher order global conditions and ultimately societal outcomes. Conclusions Techno-social simulations such as this can provide scholars and policymakers alike insights into the nonlinear, complex adaptive effects of societal co-evolution. We believe complex adaptive or evolutionary systems approaches are necessary to understand both near and potentially catastrophic, far-from-equilibrium behavior and societal outcomes across all human scales of modernization

Linking complexity economics and systems thinking, with illustrative discussions of urban sustainability

The expanding research of complexity economics has been signalling its preference for a formal quantitative investigation of diverse interactions between heterogeneous agents at the lower, micro-level resulting in emergent, realistic socioeconomic dynamics at the higher, macro-level. However, there is scarcity in research that explicitly links complexity perspectives in economics with the systems thinking literature, despite these being highly compatible, with strong connections and common historical traces. We aim to address this gap by exploring commonalities and differences between the two bodies of knowledge, seen particularly through an economics lens. We argue for a hybrid approach, in that agent-based complexity perspectives in economics could more closely connect to two main systems thinking attributes: a macroscopic approach to analytically capturing the complex dynamics of systems, and an inter-subjective interpretivist dimension, when investigating complex social-economic order. Illustrative discussions of city sustainability are provided, with an emphasis on decarbonisation and residential energy demand aspects

Computational models of emergent organisation in conflict environments

This thesis takes a multi-level computational modelling approach to develop understanding of the complex phenomena of insurgent organisation at a micro-, meso-, and macro-level. At an individual level (micro), individuals are subject to social and economic pressures that may lead to a radicalisation process in which extremist beliefs, feelings, and attitudes develop. At the societal level (macro), processes such as elections, justice and security reforms, and economic developments influence the stability of the state. In between these levels (meso), we can identify specific group-level processes that connect the attributes and skills of individuals at a communal level. In this thesis we explore the possibility of combining critical phenomena of complex systems, such as collective behaviour, feedback and self-organisation, for analysing insurgent organisation through application of different computational modelling methods. For this framework, methodologies are applied focusing on formal models to analyse the reasoning of individuals and how they perceive their environment, game theoretic and social-network models to analyse the relationships and dependencies between individuals, and agent-based and systems dynamics modelling to simulate individual and group behaviour within a certain context and analyse the development of behaviour over time. This interdisciplinary approach helped us to gain insights on how these different methodologies complement each other and enable analysis of complex phenomena specifically in the field of computational theory, conflict analysis and criminology

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex socio-technical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating them requires research methods that scale beyond the traditional areas of inquiry of Information Systems (IS) “individuals, organizations, and markets” and that deliver solutions in addition to insights. We describe an approach to address these challenges through Competitive Benchmarking (CB), a novel research method that helps interdisciplinary research communities to tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions

Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models

The resilience of a social-ecological system is measured by its ability to retain core functionality when subjected to perturbation. Resilience is contextually dependent on the state of system components, the complex interactions among these components, and the timing, location, and magnitude of perturbations. The stability landscape concept provides a useful framework for considering resilience within the specified context of a particular social-ecological system but has proven difficult to operationalize. This difficulty stems largely from the complex, multidimensional nature of the systems of interest and uncertainty in system response. Agent-based models are an effective methodology for understanding how cross-scale processes within and across social and ecological domains contribute to overall system resilience. We present the results of a stylized model of agricultural land use in a small watershed that is typical of the Midwestern United States. The spatially explicit model couples land use, biophysical models, and economic drivers with an agent-based model to explore the effects of perturbations and policy adaptations on system outcomes. By applying the coupled modeling approach within the resilience and stability landscape frameworks, we (1) estimate the sensitivity of the system to context- specific perturbations, (2) determine potential outcomes of those perturbations, (3) identify possible alternative states within state space, (4) evaluate the resilience of system states, and (5) characterize changes in system-scale resilience brought on by changes in individual land use decisions

Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models

The resilience of a social-ecological system is measured by its ability to retain core functionality when subjected to perturbation. Resilience is contextually dependent on the state of system components, the complex interactions among these components, and the timing, location, and magnitude of perturbations. The stability landscape concept provides a useful framework for considering resilience within the specified context of a particular social-ecological system but has proven difficult to operationalize. This difficulty stems largely from the complex, multidimensional nature of the systems of interest and uncertainty in system response. Agent-based models are an effective methodology for understanding how cross-scale processes within and across social and ecological domains contribute to overall system resilience. We present the results of a stylized model of agricultural land use in a small watershed that is typical of the Midwestern United States. The spatially explicit model couples land use, biophysical models, and economic drivers with an agent-based model to explore the effects of perturbations and policy adaptations on system outcomes. By applying the coupled modeling approach within the resilience and stability landscape frameworks, we (1) estimate the sensitivity of the system to context- specific perturbations, (2) determine potential outcomes of those perturbations, (3) identify possible alternative states within state space, (4) evaluate the resilience of system states, and (5) characterize changes in system-scale resilience brought on by changes in individual land use decisions