Using Qualitative Evidence to Enhance an Agent-Based Modelling System for Studying Land Use Change

This paper describes and evaluates a process of using qualitative field research data to extend the pre-existing FEARLUS agent-based modelling system through enriching its ontological capabilities, but without a deep level of involvement of the stakeholders in designing the model itself. Use of qualitative research in agent-based models typically involves protracted and expensive interaction with stakeholders; consequently gathering the valuable insights that qualitative methods could provide is not always feasible. At the same time, many researchers advocate building completely new models for each scenario to be studied, violating one of the supposed advantages of the object-oriented programming languages in which many such systems are built: that of code reuse. The process described here uses coded interviews to identify themes suggesting changes to an existing model, the assumptions behind which are then checked with respondents. We find this increases the confidence with which the extended model can be applied to the case study, with a relatively small commitment required on the part of respondents.Agent-Based Modelling, Land Use/Cover Change, Qualitative Research, Interdisciplinary Research

Reinforcement Learning Dynamics in Social Dilemmas

In this paper we replicate and advance Macy and Flache\'s (2002; Proc. Natl. Acad. Sci. USA, 99, 7229–7236) work on the dynamics of reinforcement learning in 2�2 (2-player 2-strategy) social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different transient regimes can be clearly observed. It is shown that the inclusion of small quantities of randomness in players\' decisions can change the dynamics of the model dramatically.Reinforcement Learning; Replication; Game Theory; Social Dilemmas; Agent-Based; Slow Learning

When and How to Imitate Your Neighbours: Lessons from and for FEARLUS

This paper summarises some previously published work on imitation, experimentation (or innovation) and aspiration thresholds using the FEARLUS modelling system and reports new work with FEARLUS extending these studies. Results are discussed in the context of existing literature on imitation and innovation in related contexts. A form of imitation in which land uses are selected on the criterion of their recent performance within the neighbourhood of the land parcel concerned (called here 'Best-mean Imitation'), outperforms comparably simple forms of imitation in a wide range of FEARLUS Environments. However, the choice of criterion is shown to interact with both the way the criterion is applied, and the land manager's aspiration threshold: the level of return with which they are satisfied. The implications of work with FEARLUS for the broader bodies of research discussed, and vice versa, are considered.Imitation, Innovation, Aspiration, Land-Use, Spatio-Temporal Heterogeneity

A Semantic Grid Service for Experimentation with an Agent-Based Model of Land-Use Change

Agent-based models, perhaps more than other models, feature large numbers of parameters and potentially generate vast quantities of results data. This paper shows through the FEARLUS-G project (an ESRC e-Social Science Initiative Pilot Demonstrator Project) how deploying an agent-based model on the Semantic Grid facilitates international collaboration on investigations using such a model, and contributes to establishing rigorous working practices with agent-based models as part of good science in social simulation. The experimental workflow is described explicitly using an ontology, and a Semantic Grid service with a web interface implements the workflow. Users are able to compare their parameter settings and results, and relate their work with the model to wider scientific debate.Agent-Based Social Simulation, Experiments, Ontologies, Replication, Semantic Grid

Social dilemmas: what if not everybody knows that everybody knows that everybody is rational?

In this paper social dilemmas are modelled as two-player games. In particular we model the Prisoner’s Dilemma, Chicken and Stag Hunt. When modelling these games we assume that players adapt their behaviour according to their experience and look for outcomes that have proved to be satisfactory in the past. These ideas are investigated by conducting several experiments with an agent-based simulation model in which agents use a simple form of case-based reasoning. It is shown that cooperation can emerge from the interaction of selfish case-based reasoners. In determining how often cooperation occurs, not only what Agents end up doing in any given situation is important, but also the process of learning what to do can crucially influence the final outcome. Agents’ aspiration thresholds play an important role in that learning process. It is also found that case-based reasoners find it easier to cooperate in Chicken than in the Prisoner’s Dilemma and Stag Hunt

Resilience, Panarchy, and World-Systems Analysis

The paper compares two ambitious conceptual structures. The first is the understanding of social-ecological systems developed around the term "resilience," and more recently the term "panarchy," in the work of Holling, Gunderson, and others. The second is Wallerstein's "world-systems" approach to analyzing hierarchical relationships between societies within global capitalism as developed and applied across a broader historical range by Chase-Dunn and others. The two structures have important common features, notably their multiscale explanatory framework, links with ideas concerning complex systems, and interest in cyclical phenomena. They also have important differences. It is argued that there are gaps in both sets of ideas that the other might remedy. Their greatest strengths lie at different spatiotemporal scales and in different disciplinary areas, but each also has weaknesses the other does not address, particularly with regard to the mechanisms underlying proposed cyclic patterns of events. The paper ends with a sketch for a research program within which panarchical and world-systems insights might be synthesised in the study of the "Great European Land-Grab," i.e., the expansion of European capitalism and its distinctive social-ecological systems over the past five centuries

SIZE MATTERS: LARGE-SCALE REPLICATIONS OF EXPERIMENTS WITH FEARLUS

We report on replications of early experiments with FEARLUS, using larger numbers of agents, larger numbers of land parcels, and greater network connectivity than in the original work. We find that results from the larger-scale experiments differ from the smaller environments used previously. Whilst results from small communities of agents and environments should not be ignored just because the same effect is not observed in larger communities (and indeed vice versa), this work does raise the extent to which more general conclusions can be drawn from agent-based studies involving fixed population or environment sizes.Agent-based modeling, replication, model size, network topology

Comparative Analysis of Agent-Based Social Simulations: GeoSim and FEARLUS Models

In this paper we compare models of two different kinds of processes in multi-agent-based social simulations (MABSS): military conflict within a states-system (GeoSim), and land use and ownership change (FEARLUS). This is a kind of model-to-model comparison which is novel within Multi-Agent Based Simulation research, although well-known within mathematics, physics and biology: comparing objects (in this case MABSS) drawn from distinct research domains, in order to draw out their structural similarities and differences. This can facilitate research in both domains, by allowing the use of findings from each to illuminate the other. Based on the similarities between FEARLUS and GeoSim, we conclude by identifying a new class of MABSS models based on territorial resource allocation processes occurring on a 2-dimensional space (which we define as the “TRAP2†class). The existence of the cross-domain TRAP2 class of models in turn suggests that MABSS researchers should look for other members of the class, sharing some of the properties or dynamics common to the GeoSim and FEARLUS models compared in this study: a systematic comparison of a set of related models from a range of apparently distinct domains should generate insights into both MABSS modeling, and the domains concerned.Territorial resource allocation, multi-agent-based social simulation