Friday, October 31, 3:00 p.m.
Center for Social Complexity Suite
Research Hall, Third Floor

A Topological View of Radical Innovation

Russell Thomas, PhD Student
Computational Social Science
George Mason University

ABSTRACT: This talk presents the “Topological View” -- an alternative to fitness landscapes and pre-specified state spaces for modeling innovation processes.  “The topology of innovation determines what possibilities can be seen from what vantage points, how hard it is to get from one spot in the space of possibilities to another, and so forth” [1].  The Topological View draws on a several lines of research and precursors, including Theoretical Biology [2], Evolutionary Economics [3], Organization Science [4], Sociology of Institutions [5], and Design Science [6]. While the Topological View applies to both incremental and radical innovation, its benefits as a theoretical construct stand out most clearly in the context of radical innovation: 1) it is capable of modeling emergent, multi-level, and co-evolutionary spaces; 2) it supports modeling both ontological mechanisms at a meso-level and also cognitive, social, and operational mechanisms at a micro-level; 3) it can support rigorous theoretical and empirical research, including computational simulations; and finally 4) it is parsimonious. 

For existing approaches such as fitness landscapes, the space of possibilities is pre-specified and fully characterized by global dimensions or state variables. However, in the setting of radical innovation this approach breaks down because the full space of possible innovations is not pre-specifiable and it is intrinsically emergent. In this setting, thinking and doing are reflexive, creative and coevolutionary: how innovators think shapes what becomes possible and what becomes possible shapes how innovators think. This gives rise to a paradox of indeterminate agency – innovators are largely ‘blind’ in their conceptions and actions, yet they must think and act to make change happen.

The essence of the Topological View is to model the space of possibilities locally according to proximity and neighborhood relations. Distance measures, and concepts like “near” and “far” are defined through local operations traversing the topology. “Proximity” is defined both cognitively and ontologically (i.e. operations that bring about change in the world). With this approach, the Topological View allows formal modeling of how innovators cope with this paradox the paradox of indeterminate agency, including cycles of social learning, representing and reasoning about uncertainty and ignorance, knowledge artifacts, and supporting institutions. These will be illustrated using two contemporary cases of radical institutional innovation and institutional entrepreneurship – Synthetic Biology and Cyber Security.  

A multilevel computational model is under development, drawing on Dopfter & Pott’s multi-level framework [7], Holland’s Dynamic Generated System (DGS)[8], and Padget & Powell’s Autocatalysis and Multi-level Network Folding Theory [9]. Agent-based Modeling is used at the micro level and Mechanism-based Modeling is used at the meso level (institutions). Early results will be presented. 


[1]  R. R. Nelson and S. Winter, An Evolutionary Theory of Economic Change. Harvard University Press, 1982.
[2] B. M. R. Stadler, P. F. Stadler, G. P. Wagner, and W. Fontana, “The topology of the possible: Formal spaces underlying patterns of evolutionary change,” Journal of Theoretical Biology, vol. 213, no. 2, pp. 241–274, Nov. 2001.
[3] J. Potts, The new evolutionary microeconomics: complexity, competence, and adaptive behaviour. Edward Elgar, 2000.
[4] M. H. Boisot, Knowledge Assets: Securing Competitive Advantage in the Information Economy. Oxford, UK: Oxford University Press, Dec. 1999.
[5] J. Battilana, B. Leca, and E. Boxenbaum, “How actors change institutions: Towards a theory of institutional entrepreneurship,” Academy of Management Annals, vol. 3, no. 1, pp. 65–107, 2009.
[6] J. S. Gero, “Creativity, emergence and evolution in design,” Knowledge-Based Systems, vol. 9, no. 7, pp. 435—448, Nov. 1996.
[7] K. Dopfer and J. Potts, The General Theory of Economic Evolution. Routledge, Oct. 2007.
[8] J. H. Holland, Signals and Boundaries: Building Blocks for Complex Adaptive Systems. MIT Press, Jul. 2012. 
[9] J. F. Padgett and W. W. Powell, The Emergence of Organizations and Markets. Princeton University Press, Sep. 2012.