Today, innovation has gained momentum to the detriment of history, stability, and tradition (Lane et al. 2009). The increasing social demand for innovation in the globalization era, both at a firm and a national system level, contrasts with the complex, multifaceted, systemic and unpredictable nature of innovation pro¬ cesses. Indeed, innovation is created by a network of dispersed interactions between many heterogeneous agents (i.e. firms, consumers, venture capitalists, research institutions and institutional agencies) and it largely depends on a par¬ ticular and contextual combination of different aspects (i.e. social, economic, institutional, and cultural aspects). It is also profoundly nonlinear and is subject to path dependence. It is therefore difficult to predict and impossible to plan. Since it often materi¬ alizes into ‘out¬ of¬equilibrium’ aggregative dynamics, traditional equilibrium solution¬ oriented models and standard statistical methods used for policy fore¬ casting and decisions find it difficult to capture its essence. The unpredictability of innovation can therefore cause a sense of frustration both in scientists and policy makers, given the pressurizing social expectations from taxpayers and public opinion that science-informed policy makers should do something to improve either business innovative capability, or the local/regional/national system. This is because innovation is the most crucial component of the com¬ petitive advantage of firms, regions and countries. Unfortunately, our present situation provides many examples of how tradi¬ tional policy tools fail to deal with the complexity of socio-economic systems especially for innovation (e.g. Rossi and Russo 2009). There are two kinds of deficiency. First, standard equilibrium solution-informed and standard statistical models are poorly equipped to understand the micro/local details that make a real difference for social¬ economic outcomes. As a matter of fact, there have been many puzzling outcomes that simply do not appear on the policy makers’ radar screen given that they lay outside the domain of traditional science or even the law of large numbers (Moss 2002, Miller and Page 2007). Second, traditional forecast-oriented models which prescribe ex ante solutions and recipes dramatically underestimate the entire process of policies, including the reaction of agents to policy decisions, the aggregate effect of their interac¬ tions and their systemic consequences on large spatial-temporal scales. Standard policy making models consider agents as atomized entities possessing rational expectations which individually react to a set of incentives, do not consider interactions or the mutual influence between agents and seem to take place ‘offline’ and outside the particular system involved (Finch and Orillard 2004: 5). This chapter aims first to question the state-of-the-art of policy making, in a complexity perspective. There is evidence that policy making is currently not equipped to tackle the challenge of the complexity of the innovation process. So, together with its relevant impact on scientific endeavour, the complexity per¬ spective needs a new approach to policy issues. The second aim is to provide an overview on how agent-based models (ABMs) can change policy making in a more complexity-friendly perspective. While standard policy making is an attempt to reduce or eliminate complexity, ABMs allow us to understand and ‘harness’ complexity (e.g. Axelrod and Cohen 1999). In a certain sense, when complexity is presumed and not eliminated from the policy makers’ radar, the quest for innovation policy and the quest for innovation in policy become essentially the same thing. The structure of this chapter is as follows: In the first part, we illustrate the back¬ ground and present the complexity perspective and its challenge to conventional policy making. The second part introduces applications of ABMs for policy pur¬ poses, by categorizing these approaches and illustrating some examples. We have identified two types of policy ABMs, i.e. ‘prescriptive models’ and ‘participatory modelling’, discussed their constituencies and provided two examples. Finally, we emphasize the innovative flavour that ABMs can bring to the policy arena.

Complexity-Friendly Policy Modelling / F. Squazzoni, R. Boero - In: Innovation in Complex Social Systems / [a cura di] P. Ahrweiler. - Prima edizione. - [s.l] : Routledge, 2010. - ISBN 9780415558709. - pp. 290-299

Complexity-Friendly Policy Modelling

F. Squazzoni
Primo
Writing – Original Draft Preparation
;
2010

Abstract

Today, innovation has gained momentum to the detriment of history, stability, and tradition (Lane et al. 2009). The increasing social demand for innovation in the globalization era, both at a firm and a national system level, contrasts with the complex, multifaceted, systemic and unpredictable nature of innovation pro¬ cesses. Indeed, innovation is created by a network of dispersed interactions between many heterogeneous agents (i.e. firms, consumers, venture capitalists, research institutions and institutional agencies) and it largely depends on a par¬ ticular and contextual combination of different aspects (i.e. social, economic, institutional, and cultural aspects). It is also profoundly nonlinear and is subject to path dependence. It is therefore difficult to predict and impossible to plan. Since it often materi¬ alizes into ‘out¬ of¬equilibrium’ aggregative dynamics, traditional equilibrium solution¬ oriented models and standard statistical methods used for policy fore¬ casting and decisions find it difficult to capture its essence. The unpredictability of innovation can therefore cause a sense of frustration both in scientists and policy makers, given the pressurizing social expectations from taxpayers and public opinion that science-informed policy makers should do something to improve either business innovative capability, or the local/regional/national system. This is because innovation is the most crucial component of the com¬ petitive advantage of firms, regions and countries. Unfortunately, our present situation provides many examples of how tradi¬ tional policy tools fail to deal with the complexity of socio-economic systems especially for innovation (e.g. Rossi and Russo 2009). There are two kinds of deficiency. First, standard equilibrium solution-informed and standard statistical models are poorly equipped to understand the micro/local details that make a real difference for social¬ economic outcomes. As a matter of fact, there have been many puzzling outcomes that simply do not appear on the policy makers’ radar screen given that they lay outside the domain of traditional science or even the law of large numbers (Moss 2002, Miller and Page 2007). Second, traditional forecast-oriented models which prescribe ex ante solutions and recipes dramatically underestimate the entire process of policies, including the reaction of agents to policy decisions, the aggregate effect of their interac¬ tions and their systemic consequences on large spatial-temporal scales. Standard policy making models consider agents as atomized entities possessing rational expectations which individually react to a set of incentives, do not consider interactions or the mutual influence between agents and seem to take place ‘offline’ and outside the particular system involved (Finch and Orillard 2004: 5). This chapter aims first to question the state-of-the-art of policy making, in a complexity perspective. There is evidence that policy making is currently not equipped to tackle the challenge of the complexity of the innovation process. So, together with its relevant impact on scientific endeavour, the complexity per¬ spective needs a new approach to policy issues. The second aim is to provide an overview on how agent-based models (ABMs) can change policy making in a more complexity-friendly perspective. While standard policy making is an attempt to reduce or eliminate complexity, ABMs allow us to understand and ‘harness’ complexity (e.g. Axelrod and Cohen 1999). In a certain sense, when complexity is presumed and not eliminated from the policy makers’ radar, the quest for innovation policy and the quest for innovation in policy become essentially the same thing. The structure of this chapter is as follows: In the first part, we illustrate the back¬ ground and present the complexity perspective and its challenge to conventional policy making. The second part introduces applications of ABMs for policy pur¬ poses, by categorizing these approaches and illustrating some examples. We have identified two types of policy ABMs, i.e. ‘prescriptive models’ and ‘participatory modelling’, discussed their constituencies and provided two examples. Finally, we emphasize the innovative flavour that ABMs can bring to the policy arena.
Complexity; Policy Modelling; Agent-Based Models
Settore SPS/07 - Sociologia Generale
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/661106
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