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Agent-based simulation for science, technology, and innovation policy

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Abstract

Policymaking implies planning, and planning requires prediction—or at least some knowledge about the future. This contribution starts from the challenges of complexity, uncertainty, and agency, which refute the prediction of social systems, especially where new knowledge (scientific discoveries, emergent technologies, and disruptive innovations) is involved as a radical game-changer. It is important to be aware of the fundamental critiques, approaches, and fields such as Technology Assessment, the Forrester World Models, Economic Growth Theory, or the Linear Model of Innovation have received in the past decades. It is likewise important to appreciate the limitations and consequences these diagnoses pose on science, technology and innovation policy (STI policy). However, agent-based modeling and simulation now provide new options to address the challenges of planning and prediction in social systems. This paper will discuss these options for STI policy with a particular emphasis on the contribution of the social sciences both in offering theoretical grounding and in providing empirical data. Fields such as Science and Technology Studies, Innovation Economics, Sociology of Knowledge/Science/Technology etc. inform agent-based simulation models in a way that realistic representations of STI policy worlds can be brought to the computer. These computational STI worlds allow scenario analysis, experimentation, policy modeling and testing prior to any policy implementations in the real world. This contribution will illustrate this for the area of STI policy using examples from the SKIN model. Agent-based simulation can help us to shed light into the darkness of the future—not in predicting it, but in coping with the challenges of complexity, in understanding the dynamics of the system under investigation, and in finding potential access points for planning of its future offering “weak prediction”.

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Fig. 1

Source: Ahrweiler et al. 2015

Fig. 2

Source: DG INFSO

Fig. 3

Source: Final Report 2011; European Commission. (Color figure online)

Fig. 4

Source: Final Report 2011; European Commission. (Color figure online)

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Notes

  1. This study states that the innovation output of Irish business may be negatively affected by R&D links to universities, and that future national prosperity may be undermined by continued investment in university research and university-industry collaboration. The resolution of the paper is that attempts to build local linkages and clusters are a wasted effort.

  2. Intelligent does not equal “rational”. The term means that the agents are equipped with mechanisms to develop and choose between strategies and options to act according to their displayed capacities observed in the empirical realm.

  3. Examples about how social theories inform SKIN agents: the implementation of theories of Organizational Learning presented by March/Olsen and Argyris/Schön in Gilbert et al. 2007; or the implementation of organizational theory approaches presented by W.W. Powell concerning mechanisms of partner choice in networks (Ahrweiler et al. 2011a, b).

  4. EU Cost Action “KnowEscape: Analyzing the dynamics of information and knowledge landscapes” www.knowescape.org shows the scope of these methods for description and analysis stemming from e-humanities and BigData technologies.

  5. Some features of complex systems will not go away, however. Among these are all “surprise” features based on external shocks, agency, and creativity (cf. Ahrweiler 2010). They will always refute the absolute reliability of any predictions. The methods above only allow “weak prediction” in the sense outlined.

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    Petra Ahrweiler

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Ahrweiler, P. Agent-based simulation for science, technology, and innovation policy. Scientometrics 110, 391–415 (2017). https://doi.org/10.1007/s11192-016-2105-0

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