Aktivität: Vortrag ohne Tagungsband / Vorlesung › Vortrag ohne Tagungsband
Over the recent past, we can observe increasing interest in the ex-ante impact assessment of research policy, mainly related to the growing importance of accountability and limited budgets. However, existing approaches to ex-ante impact assessment often lack quantitative methods that go beyond simple extrapolations of current trends. This paper addresses this gap by proposing an empirical agent-based model (ABM) to simulate the complex micro-dynamics of knowledge generation under specific policy regimes. With our emphasis on the empirical calibration of ABMs, we intend to conduct Scenario simulations applicable to real world contexts - in this study illustrated by means of an ABM on the Austrian biotechnology sector. In our model, effects of public research policy on the knowledge-related output - measured by the patent portfolio at the system level - are under scrutiny. By this, the study contributes to the literature on ABMs in several aspects: Building on an existing concept of knowledge representation, we advance the model of individual and collective knowledge generation in firms by conceptualising policy intervention and corresponding output indicators. Furthermore, we go beyond symbolic ABMs of knowledge production by using empirical patent data as knowledge representations, adopt an elaborate empirical initialisation and calibration strategy using company data, and utilize econometric techniques to generate a sector-specific fitness function that determines the model output. With this model, we are able to conduct scenario analyses on effects of different public research funding schemes in the field of biotechnology. The results demonstrate that an empirically calibrated and transparent model design increases credibility and robustness of the ABM approach in the context of ex-ante impact assessment of public research policy.
28 Mai 2015 → 29 Mai 2015
IWcee15 - International Workshop on Computational Economics and Econometrics
Bekanntheitsgrad - verpflichtend einzutragen!
Ehemaliges Research Field - Innovation Systems and Policy