Martin Ihrig

Epistemology of modeling and simulation

While ontology deals with the question of being or existence, epistemology deals with the question of gaining knowledge. This panel addresses the challenge of how we gain knowledge from modeling and simulation. What is the underlying philosophy of science of M&S? What are our canons of research for M&S? Is it sufficient to apply the foundational methods of the application domains, or do we need to address these questions from the standpoint of M&S as a discipline? The invited experts illuminate various facets from philosophical, mathematical, computational, and application viewpoints.

Investigating open innovation strategies: a simulation study

Taking a knowledge-based approach to innovation, we distinguish between four different innovation strategies that vary in how knowledge is sourced and exploited: open innovation (OI), closed innovation, outbound OI, and inbound OI. We build an agent-based simulation model to explore the competitive performance profiles and innovation creation potential of these different strategies. Our simulation research allows for modelling the micro-foundations of open innovation and for studying innovation strategies in different market conditions. We find that the relative financial payoffs associated with the different innovation strategies vary over time: some strategies win out in the short-term, others in the long-term. Our results also suggest that a focused closed innovation strategy can lead to higher financial performance when all the resources are concentrated on internal R&D and commercialisation. Open innovation, and in particular the inbound side of it, is a beneficial long-term strategy, whereas closed innovation may be more profitable in the short run.

Organization as Information – a Space Odyssey

This Vita Contemplativa has been written in recognition of Max Boisot, who died in 2011. It reflects on his work and its contributions to organization studies and beyond. Boisot created a knowledge-based lens for studying complex organizational phenomena. He argued that the ways in which agents process information have fundamental implications for our understanding of groups and organizations within the emerging knowledge society. He articulated this argument with a set of elegant conceptual frameworks that have been widely used by scholars and practitioners to address the dynamics of information, knowledge and learning and the organization of social and scientific activity. We provide an overview of Boisot’s conceptual frameworks before reviewing the impact of his work in the organizational field, which included contemporary developments in China, the role of information in organizations, and more recently organizational complexity and the management of Big Science at CERN. Boisot’s analysis opens up a number of avenues for further development, which are also discussed.

Simulating Entrepreneurial Opportunity Recognition Processes: An Agent-Based and Knowledge-Driven Approach

This chapter describes how we abstract a complex phenomenon– opportunity recognition–and build a simulation model for studying strategic entrepreneurship in a knowledge-based economy. We develop an agent-based simulation tool that enables researchers to explore entrepreneurial strategies, their associated financial payoffs, and their knowledge creation potential under different environmental conditions. Opportunity recognition processes can be analyzed in detail both on a micro- and macro-level. To illustrate the modeling capabilities of the tool, we conduct some basic simulation runs and model three distinct entrepreneurial strategies–the innovator, the inventor, and the reproducer–and compare their knowledge progression and financial performance profiles. The software allows us to study the individual and the societal level effects that arise from competitive agent behavior in both national and international settings. It can account for international knowledge spillovers that occur in a globalized knowledge economy. The simulation can be used to conduct innovative research that will result in theory-driven hypotheses that can inform corporate and public-sector decision makers, which would be difficult to derive from empirical analyses.

SimISpace2 : A Simulation Platform for Exploring Strategic Knowledge Management Processes

SimISpace2 is an agent-based simulation environment designed to simulate strategic knowledge management processes, in particular knowledge flows and knowledge-based agent interactions. It serves as a general knowledge management engine that, through a user-friendly graphical interface, can be adapted to a wide range of knowledge-related applications. Its purpose is to improve our understanding of how knowledge is generated, diffused, internalized and managed by individuals and organizations, under both collaborative and competitive learning conditions.

A New Research Architecture for the Simulation Era

This chapter proposes a novel research architecture for social scientists who want to employ simulation methods. The new framework gives an integrated view of a research process that involves simulation modelling. It highlights the importance of the theoretical foundation of a simulation model and shows how new theory-driven hypotheses can be derived that are empirically testable. The paper describes the different aspects of the framework in detail and shows how it can help structure the research efforts of scholars interested in using simulations.