DOING POLICY IN THE LAB! OPTIONS FOR THE FUTURE USE OF MODEL-BASED POLICY ANALYSIS FOR COMPLEX DECISION-MAKING

For models to have an impact on policy-making, they need to be used. Exploring the relationships between policy models, model uptake and policy dynamics is the core of this article. What particular role can policy models play in the analysis and design of policies? Which factors facilitate (inhibit) the uptake of models by policy-makers? What are possible pathways to further develop modelling approaches to better meet the challenges facing agriculture today? In this paper, we address these issues from three different points of view, each of which should shed some light on the subject. The first point of view discusses models in the framework of complex adaptive systems and uncertainty. The second point of view looks at the dynamic interplay between policies and models using the example of modelling in agricultural economics. The third point of view addresses conditions for a successful application of models in policy analysis.modelling, complexity, participatory modelling, policy analysis, model use, Agricultural and Food Policy, Research Methods/ Statistical Methods,

Using Computational Agents to Design Participatory Social Simulations

In social science, the role of stakeholders is increasing in the development and use of simulation models. Their participation in the design of agent-based models (ABMs) has widely been considered as an efficient solution to the validation of this particular type of model. Traditionally, "agents" (as basic model elements) have not been concerned with stakeholders directly but via designers or role-playing games (RPGs). In this paper, we intend to bridge this gap by introducing computational or software agents, implemented from an initial ABM, into a new kind of RPG, mediated by computers, so that these agents can interact with stakeholders. This interaction can help not only to elicit stakeholders' informal knowledge or unpredicted behaviours, but also to control stakeholders' focus during the games. We therefore formalize a general participatory design method using software agents, and illustrate it by describing our experience in a project aimed at developing agent-based social simulations in the field of air traffic management.Participatory Social Simulations, Agent-Based Social Simulations, Computational Agents, Role-Playing Games, Artificial Maieutics, User-Centered Design

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

An Objective-Based Perspective on Assessment of Model-Supported Policy Processes

Simulation models, being in use for a long time in natural sciences and engineering domains, are diffusing to a wider context including policy analysis studies. The differences between the nature of the domain of application, as well as the increased variety of usage partially induced by this difference naturally imply new challenges to be overcome. One of these challenges is related to the assessment of the simulation-based outcomes in terms of their reliability and relevance in the policy context being studied. The importance of this assessment is twofold. First of all, it is all about conducting a high quality policy study with effective results. However, the quality of the study does not necessarily imply acceptance of the results by the clients and/or colleagues. This problem of policy analysts increases the importance of such an assessment; an effective assessment may induce the acceptance of the conclusions drawn from the study by the clients and/or colleagues. The main objective of this paper is to introduce an objective-based assessment perspective for simulation model-supported policy studies. As a first step towards such a goal, an objective-based classification of models is introduced. Based on that, we will discuss the importance of different aspects of the assessment for each type. In doing so, we aim to provide a structured discussion that may serve as a sort of methodological guideline to be used by policy analysts, and also by clients.Simulation, Validation, Model Assessment, Policy Analysis, Model Typology

The Interactive Lecture: Teaching and Learning Technologies for Large Classrooms

Conventional lectures in large classrooms are connected to fundamental didactic problems due to a lack of interactivity and feedback opportunities. In an interactive lecture each student is equipped with a light-weight, mobile device that can be used to interact with the lecturer during the lesson, thus creating an additional channel of communication. These devices support new teaching and learning paradigms such as participatory simulations. In this paper, we present our experiences with the usage of mobile devices in lectures. After discussing the didactic benefits of interactive lectures, we introduce the software toolkits used in our scenarios, we highlight selected tools like a quiz tool or a support tool for participatory simulation, and present major results from six studies we have conducted

Asking the Oracle: introducing forecasting principles into agent-based modelling

This paper presents a set of guidelines, imported from the field of forecasting, that can help social simulation and, more specifically, agent-based modelling practitioners to improve the predictive performance and the robustness of their models. The presentation starts with a discussion on the current debate on prediction in social processes, followed by an overview of the recent experience and lessons learnt from the field of forecasting. This is the basis to define standard practices when developing agent-based models under the perspective of forecasting experimentation. In this context, the guidelines are structured in six categories that correspond to key issues that should be taken into account when building a predictor agent-based model: the modelling process, the data adequacy, the space of solutions, the expert involvement, the validation, and the dissemination and replication. The application of these guidelines is illustrated with an existing agent-based model. We conclude by tackling some intrinsic difficulties that agent-based modelling often faces when dealing with prediction models.project Social Ambient Assisting Living - Methods (SociAAL), supported by Spanish Council for Science and Innovation, with grant TIN2011-28335-C02-01 and the Spanish MICINN project CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010). We also thank the support from the Programa de Creación y Consolidación de Grupos de Investigación UCM-BSCH, GR35/10-A (921354