IPSE Sub-Project 1:

SP1: IRU Computational Policy Lab

(using data collected and provided by SP2 and SP3)

Objectives

This SP develops a Computational Policy Lab at IRU (involved participants: IRU, CASL, MIT, CRESS, DE Hohenheim Ec.) to investigate Ireland’s research, innovation and commercialisation ecosystem in the Smart Economy. For monitoring the Smart Economy, it incorporates methods such as social network analysis (IRU, Geary, CASL) and computational text analysis (IRU). The Lab will present opportunities for changing the ‘rules’. Innovation policy research (IRU, QUB Management) will suggest multiple scenarios that can be developed and simulated. Policies can be experimentally tested before implementation.

Description of work

We use agent-based modelling (ABM) – a powerful and innovative methodology which gains more and more prominence in the scientific community – to identify and understand the effects of certain innovation policy strategies and their associated knowledge dynamics. In contrast to conventional methods of social research, ABM provides us with a “computational laboratory” that is capable of dealing with the high complexity and non-linearity of the processes under study. The modelling tasks of all empirical sub-projects will provide the pilot studies for IRU’s Computational Policy Lab. Lab users address research questions that involve different levels of the Irish ecosystem: from start-up firms (micro level) to academic-industry partnerships (meso level) to whole sectors or regions (macro level). IRU’s Lab informs simulation with large empirical data sets.

Tasks

Provide a conceptual framework for computational policy research

Developing a conceptual framework for the combined application of knowledge mapping techniques, computational network analysis (IRU, Geary, DE Hohenheim Ec.) and agent-based modelling (IRU, CRESS), we will present a novel methodology that allows for a fully integrated and more comprehensive understanding of the governance of complex innovation systems than has hitherto been achieved.

Translate empirical results into formal approaches

In IPSE, we start from qualitative and quantitative empirical observations on knowledge generation and diffusion in innovation networks, and on policy goals. These are translated into formal approaches for computational investigation (IRU, CASL, CRESS, DE Hohenheim Ec.).

Create a simulation platform for IPSE case studies

By creating a simulation platform, which reflects the dynamic evolution of innovation network structures and innovation performance (IRU, CRESS, DE Hohenheim Ec.), the IPSE project will allow the modelling of interactions between existing policies and business practices, future policy scenarios, and alternative business strategies.

Identify access points, strategies and rehearsal methods

We need to identify access points for policy advice and firm management to find the "optimal" design of strategies enabling policy makers and corporate managers to rehearse their strategies before realisation. In IPSE’s scenario modelling we have to identify areas, which need intervention (IRU), to specify the desired states launched by Irish innovation policies, to find the regulating mechanisms, to suggest policy formation and implementation strategies, and to control and evaluate the robustness of the policies proposed.

Translate findings into policy advice

The results of this work are re-translated into conceptual frameworks that are useful and comprehensible to potential users, above all policy makers (IRU). This task has to deliver a communicable but complexity-adapted way to support policy design and analysis in innovation networks. Developing and communicating scenarios for the future relationship between socio-economic, environmental, and social issues will reduce risks and allow the optimal allocation of resources for innovation policy makers on all levels.