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Computerized models: tools for assessing the future of complex systems?

Martin K. van Ittersum and Barbara Sterk

INTRODUCTION

Models are commonly used to make decisions. At some point all of us will have employed a mental model, that is, a simplification of reality, in an everyday situation. For instance, when we want to make the best decision for the environment and consider whether to buy our vegetables in a large supermarket or a local farm shop, we will use our own mental model of what is good, and less good, for the environment. But it was the advent of computers that gave a boost in particular to quantitative models. They have been on the scene roughly since the Second World War. Since the 1950s, engineers have studied complex dynamic systems using computer models, inspiring biologists to apply similar techniques in their disciplines. Such models assist in understanding the behaviour of a system, that is, a limited part of reality that contains interrelated elements. This understanding generally refers to how the different elements (components) of a system interact and determine the state of the system at a certain moment, as well as how it may change over time. Once this understanding of historical and present behaviour has been achieved, models are used to forecast future states of the system.

In reality, different computer models serve different policy formulation purposes. As the literature uses a variety of often inconsistent terms to categorize computer models, in this chapter we first try to shed some light on terminology, and more importantly on different classes of computerized models and their purposes in forecasting future states of systems (Section 2). We then introduce the various ways in which computer models can be used in a policy formulation process and how this relates to other tools as described in this book (Section 3). To properly understand the role of computer models in policy formulation processes we need to have a closer look at what evidence and knowledge they deliver to such processes, which is the subject of Section 4.

After these introductory sections we are ready to have a somewhat more detailed look at practical cases in which computer models played a role in policy formulation processes to derive insights from hindsight. Modesty is justified when it comes to the use of models in such processes: while almost every scientific paper presenting a model or application in a case study claims (potential) usefulness for decision and policymaking processes, few have documented real-life applications with a demonstrated analysis of policy impact. This is not to say that models are rarely used in societal processes, but rather that analysis and documentation of the (non-)use in the literature is scarce. In Section 5 we therefore present lessons learned from a number of case studies in which models did play an important role and from this we try to achieve a deeper understanding of the utility of computer models in policy formulation, their users, and when and how models are employed in practice. Although we focus on cases where models have been used, the reasons why in many other cases they have not been used logically follow from the analysis, because one or several of the conditions for use have not been met. In Section 6 we conclude with a discussion of key factors that are important in the effective use of computerized models in policy formulation processes, and highlight possible new research on this important, policy-relevant topic.

 
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