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320 F. Beckenbach, R. Briegel cognitive processes accrued to real human beings. This necessitates to enrich this modeling of human cognitive processes by picking up insights from sciences investigating the behavior of real human beings like modern (cognitive) psychology, neuroscience as well as experimental economics. According to the findings in these behavioral sciences limited short-term memory capacities, patterns of perception and understanding (like frames, schemata, scripts, mental maps etc.) as well as the cognitive economizing included therein (manifest in the prominent role of routines and habits), different types of learning as well as the process of selecting and weighing goals seem to be an essential part of the (limited) capabilities of human beings to act (cf. Camerer et al. 2005; Gintis 2000). Multi-agent models are on one side a framework predestined for incorporating these insights (cf. Sun 2001, 2006); on the other side there is a constraint in that these insights have to be translated into a computable framework and in that incorporating the above mentioned insights should contribute to the emergent property at stake. Hence, the problems of arbitrariness arising if Pandora’s boxof bounded rationality (Simon 2000) is opened can at least be constrained in a multiagent modeling framework: there is firstly a need of computability and secondly a need for plausibility of the assumptions borrowed from the modern behavioral sciences for the given modeling context. According to this strand of thought agent-based modeling has been applied to a lot of phenomena belonging to the realm of economics (cf. the overview in Tesfatsion 2002; Tesfatsion and Judd 2006), especially market processes (Kirman and Vriend 2001; Farmer 2001; Luna and Stefansson 2000), technological change (Dawid 2006; Fagiolo and Dosi 2003), network dynamics (Wilhite 2001) and organizations (Chung and Harrington 2006; Klos and Nooteboom 2001; Prietula et al. 1998). The same is true for ecological phenomena resulting from human impacts. Here a special emphasis has been laid on common pool resources and land use patterns (cf. the overview in Janssen 2002, 2004). Only recently the question has been raised if agent-based modeling is an appropriate tool for analyzing climate change adaptation and sustainability issues (cf. Balbi and Giupponi 2009 for a survey). These studies are a starting point for revealing the potential of multi-agent modeling related to real human beings. Especially with regards to economic phenomena there are still multiple opportunities to unfold and to incorporate ideas about bounded rationality, non-linear interaction in markets and ‘far-fromequilibrium’-regularities on the macro-level into a multi-agent framework. In the following section we will demonstrate how the dynamics of economic aggregates can be explained by agents and their interaction both being based on modern behavioral insights. 3 Agent-based analysis of innovation dynamics In this context economic growth (in aggregated monetary terms) is conceptualized as an emergent property (as explained in section 2). That means growth cannot be derived by simply analyzing a representative economic entity or by only aggregating all entities of an economy under investigation. Hence, there is no simple functional relationship between economic inputs (like ‘capital’, ‘labor’ etc) and outputs (like the
Multi-agent modeling of economic innovation dynamics and its implications 321 produced amount of commodities and services expressed in monetary terms). 5 As already mentioned in section 1 there are (at least) two basic reasons for being skeptical about such a simple production equation: the behavioral complexity of individuals (or agents) investing capital, labor etc. and the non-linear interaction effects between these agents. The agents we will focus on are firms. Undoubtedly they play the most important part for generating growth in the economy. In the case of firms the adaptive capacity required for the agents in a multi-agent framework (cf. section 2) is given in terms of endowments (finance, knowledge) and different modes of action (like routines, choices etc.). The latter circumscribe different in-house capacities to perceive a situation, to use information as well as knowledge and to select an activity. Furthermore firms have an adaptive potential in determining their way of interaction with other firms (indirect market interaction, direct cooperation or loose network relations). Due to empirical evidence on one side and the corresponding shortcomings in modern economic growth theory on the other side we will confine ourselves to analyze the creation of novelties (innovation and imitation) by firms as a driver for economic growth. Creating novelties is a temporary (very resource-consuming) activity of firms involving a high risk of failure. Accordingly novelty creating activities of firms are triggered by specific behavioral and competitive conditions. 6 If it is successful a novelty will generate additional activities; at the same token a devaluation or substitution of old activities will take place. 7 Hence, the overall effect of novelties for aggregates of economic activities is by no means trivial. For grasping the relationship between novelty creating activities of agents and the growth of economic aggregates a multi-level approach is suggested (cf. Fig. 1). The first level specifies the triggering conditions for novelty creating activities for the agents i.e. firms. Here the behavioral elements and the modes of actions for the firms are portrayed by using an agent-based approach. On the second level the consequences of successful innovations and imitations in a given sector of economic activities are dealt with. This depends on the frequency of successful novelties and on the way they diffuse in that sector. We use an agent-related functional approach applying difference equations for depicting the stylized facts of the diffusion dynamics. Finally on the third level sectoral interdependencies are taken into account. By referring to an accounting approach (input/output-table) the diffusion effects of novelties in one sector for other sectors can be traced. Only if these different levels of economic dynamics are separated as well as related to each other it is possible to derive aggregate effects of novelties for the whole economy. This 5 To assume one or several simple relations (e.g. as aggregated equations or as production functions) is still the state of the art in modern growth theory (cf. e.g. Fine 2000). No attempt has been made to show that these are empirically and methodologically legitimate assumptions. Using evolutionary methodologies allowing disaggregate defines an alternative path for conceptualizing growth in general and especially environmental innovation (cf. Frenken and Faber 2009). In the sequel we will follow this path of economic thinking. 6 This essential feature of novelty creation is ignored in theories of ‘endogenous growth’ (e.g. Romer 1990) where r&d is a continuous and riskless activity separated from other firm activities. 7 To ignore this (non-linear) interdependency of new and old activities (and of their outcomes respectively) is another failure of most contributions to ‘endogenous growth theory’: here every innovation is immediately patented and the new activity is simply an add-on for total production (cf. Romer 1990).
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228 R. Bleischwitz economics and po
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246 R. Walz 1 Introduction Competen
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252 R. Walz In addition to exports,
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