Rigorous Theories of Business Strategies in a World of Evolving Knowledge

January 23 - 26, 2012
Max Planck Institute for Mathematics in the Sciences

Outline of the Workshop

We believe that creating new formal theory in management science is a great opportunity. The reason is not so much that we take position in the ongoing discussion whether management science should be formal theory or verbal theory (we believe, it should be both), but we argue that disparate strands of management science can be integrated if we find better formal foundations. The ultimate goal of management science should be to provide tools and insights how creative, competitively healthy, and robust organizations can be built. We point out below why we think formal theory is one major contribution to achieve this, and how the workshop will contribute.

During the workshop, we will focus on some concrete modeling innovations. The last day will be spend to discuss 'what quality formal modeling research' in management science should be.

Formal modeling has usually been applied to issues of maximizing or finding optima in the context of complete specification of alternatives, but ideas in 'the Behavioral Theory of the Firm' first proposed almost 60 years ago have continued to develop as potentially fertile arenas for formal models incorporating less restrictive (more realistic) assumptions.
The scientific subfield 'organization science' of management science emerged in the second half of the last century from thoughts of Herbert Simon. Simon, Cyert and March argued that organizations in the real world have to make decisions at conditions that typically deviate from the assumptions of neoclassical economics. In particular, the set of possible choices, the consequences of choices, and probabilities of outcomes are unknown to most firms.
Scholars started exploring how actual firms make decisions, given that they do not know the set of relevant choice alternatives and outcomes. But formal theory, most importantly game theory, still mostly rests on the assumptions that  the possible outcomes and their probabilities are known to all participants.

We therefore need to systematically incorporate the cognitive conditions and limitations of the players into new game theoretical models. Modeling unawareness is one important step in this direction, as games with unawareness reflect the real world condition that knowledge is always incomplete, and evolves as interaction unfolds. 'Asymmetric awareness' in game theory models mirrors the real world condition when one interacting party 'sees' possibilities that the other doesn't.
This modeling has the potential to contribute to integrating 'knowledge based theories' and 'cognition based theories' in management science with management theories that emerged from neoclassical economics. However, to achieve this, we need to both work on the game theoretical tools and their applications to business strategy. The theoretical tools, like awareness structures in games, are not yet developed far enough. Before they can be applied to business problems,  some important conceptual questions need to be resolved (see below). The workshop will offer a platform to work on these questions as well as the application of the formal structures to business strategy questions.

It is often argued that novelties by definition cannot be anticipated, and therefore, there cannot exist any relevant models. We believe, however, that much progress can be made in two directions. On one hand, certain firms can create and shape new markets. We may study which cognitive abilities allow them to do this, and how these differ from the cognitive abilities of other players. On the other hand, game theoretical models with unawareness components can tell us how potentially superior cognitive insights can be exploited in markets. Here, the players in the models can be equipped with optimal cognitive capabilities, while possessing different and limited knowledge.

Observing the current strategic practice of successful global firms like Apple and Google suggests that shaping new business environments is a process over time that often starts with a new insight on the future nature of a business, or stated differently, an insight on 'new business models', not only a technological innovation. The innovators 'see' how value can be captured and how the architecture of business models in the industry can be changed and shaped.
During the workshop, we will work on new formal expressions of knowledge, and its strategic use to shape the evolution of a business field. The tools offered by standard decision theory (Savage, 1954) and the behavioral decision theory program may be useful normative and descriptive tools in environments of low complexity. But we are lacking a decision theory (both normative and descriptive) that accounts for decision environments of high complexity. To develop an agenda what we need to do to create a decision theory that is a useful normative tool and has descriptive power in complex environments is an aim of the workshop.
A corresponding question is how business models, and their evolution, can be modeled. We wish to link new rigorous models of firms' knowledge with the formal expressions of business models by Ramon Casadesus-Masanell (HBS), and formal expressions of 'value coalitions' in industries (Adam Brandenburger, NYU), and their changes over time. We hope to be able to model how firms with insights of new business models can enter an existing business field and re-shape it in strategic interaction.

At the last day of the workshop, we wish to discuss what 'quality formal modeling research' in the academic field of strategic management should be. We think that quality formal modeling research in strategy should combine the quality criteria from both economics and organization science. That is, the research should be rigorous, and general analytical results should be obtained. But on the other hand, the research should produce tools that help us to deal with the real world in a creative and reflected way. While the tools that were produced by neoclassic economists certainly are rigorous, they often make too tight assumptions in terms of the knowledge of the interacting parties, leaving little room to study how competitive advantages may come about as a result of superior cognitive insights of individuals.


  • Adner, R., Polos, L., Ryall, M., Sorenson, O., The Case for Formal Theory, Volume 34, Number 2 / 2009
  • Barney, J. 1986. Strategic factor markets: expectations, luck, and business strategy., Management Science 32(10): 1231-1241.
  • Brandenburger. A., Vinokurova, N. 2011. Comment on 'Towards a Behavioral Theory of Strategy.', Organization Science, forthcoming
  • Brandenburger, A., Stuart, H. 2007. Biform Games. Management Science, Vol. 53, No. 4, April 2007, pp. 537-549
  • Byrne, R. 2005. The Rational Imagination. How People Generate Alternatives to Reality. MIT Press.
  • Casadesus-Masanell, R., Larson, T., 2010. Competing through Business Models, Harvard Business School Module Note.
  • Denrell, J. and B. Kovacs (2008). "Selective Sampling of Empirical Settings in Organizational Studies". Administrative Science Quarterly, 53 (March): 109-144.
  • Ehrig, T. 2011. The Difference Between Learning and Imagination. Working Paper. Max Planck Institute for Mathematics in the Sciences.
  • Gigerenzer, G., Brighton, H., 2009. Homo Heuristicus: Why Biased Minds Make Better Inferences, Topics in Cognitive Science, Volume 1, Issue 1, pages 107-143
  • Gigerenzer, G., Selten, R., 2001. Bounded Rationality: The Adaptive Toolbox, MIT Press.
  • Heifetz, A., Meier, M., Schipper, B. 2010. Dynamic Unawareness and Rationalizable Behavior. Working Paper. UC Davis.
  • Jacobides, M, Knudsen, T., Augier, M. 2006. Benefiting from innovation: Value creation, value appropriation and the role of industry architectures. Research Policy, 35:1200-1221.
  • Jacobides, M., Tae, J. 2009. Who becomes the winner in an industry? How dynamics within a segment shape the segment's position in the industry architecture. Conference Paper. Druid Conference.
  • Jaynes,G. Bretthorst, L. 2003. Probability Theory: The Logic of Science. Cambridge University Press.
  • Johnson-Laird, P. 1983. Mental Models. Harvard University Press.
  • Johnson-Laird, P. N.; Girotto, V.; Legrenzi, P. 2004. Reasoning From Inconsistency to Consistency. Psychological Review, Vol 111(3), 640-661.
  • Johnson-Laird, P. 2010.  Proceedings of the National Academy of the Sciences, vol. 107, no. 43, pages 18243-18250.
  • Kahneman, D., Solvic, Tverksy, A., 1982, Judgment under uncertainty: heuristics and biases. Cambridge University Press.
  • Lizier, J., Atay, F., Jost, J. 2011. On information storage, loop motifs and clustered structure. Working paper, to be released soon.
  • Lu, W., Atay, F., Jost, J. 2007 Synchronization of discrete-time
    dynamical networks with time-varying couplings, SIAM J.Math.Anal. 39,
  • Lu, W., Atay, F., Jost, J. 2011. Consensus and synchronization in
    discrete-time networks of multi-agents with stochastically switching
    topologies and time delays, Networks and Heterog. Media 6, 329-349
  • MacDonnell, G., Ryall, M. 2004. How do value creation and competition determine whether a firm appropriates value?, Management Science,  50,10: 1319-1333.
  • Prahalad CK, Bettis R. 1986. The dominant logic: a new linkage between diversity and performance. Strategic Management Journal7(6): 485-501.
  • Ryall, M. 2009. Causal Ambiguity, Complexity, and Capability-Based Competitive Advantage, Management Science, Volume 55 Issue 3, March 2009
  • Ryall, M. 2009b. The Case for Formal Theory (with Ron Adner, Laszlo Polos, and Olav Sorenson), Academy of Management Review
  • Savage, L. 1954. The Foundations of Statistics. John Wiley and Sohns.
  • Simon H. 1997. Administrative Behavior. A study of decision-making processes in administrative organizations. The Free Press: New York.

Date and Location

January 23 - 26, 2012
Max Planck Institute for Mathematics in the Sciences
Inselstraße 22
04103 Leipzig
see travel instructions

Program Comittee

Timo Ehrig
Max-Planck-Institut für Mathematik in den Naturwissenschaften

Michael Jacobides
London Business School

Jürgen Jost
Director, Max-Planck-Institut für Mathematik in den Naturwissenschaften

Massimo Warglien
University Ca' Foscari, Venice

Administrative Contact

Antje Vandenberg
Max-Planck-Institut für Mathematik in den Naturwissenschaften
Contact by Email

05.04.2017, 12:42