Theories of Innovation: Techniques for Accelerating Innovation Part 2

Published on INFORMS Analytics Magazine (Joseph Byrum)

Author’s note: This series Techniques for Accelerating Innovation will explore a new approach to innovation grounded in the Adaptive Response Framework—observe, orient, decide, act—which helps organizations navigate complexity with agility. Part One in this series began by examining the limits of traditional methods to innovating.

This is the second installment of a series that seeks to develop a new approach to innovation by understanding the limits of the current methods of innovating. There are two popular frameworks for understanding innovation. The first is game theory, in which innovation is seen in the context of competition or cooperation between or among agents. The other is systems theory, which sees innovation as a system within larger systems involving many agents. Game theory and systems theory are related, but game theory tends to be simpler, often focusing on one set of agents rather than several.

Game Theory: Innovation as a Game

The point of game theory is to reduce economics down to the level of exchange, or, one might say, decision. Using game theory, a player can apply rules of statistics and probability to predict what another player (or other players) will do and change or retain a strategy accordingly [1].

Games can be cooperative or noncooperative (competitive). There are at least 37 common game types [2] and they are mostly (but not all) competitive. As noted by Baniak and Dubina a decade ago, “The majority of literature on game-theoretic application to innovation considers rivalry aspects of innovation activities” [3]. 

Mathematicians John von Neumann and Oskar Morgenstern wrote about this first in 1953 in their book, “The Theory of Games and Economic Behavior” [4]. By their era, the field of economics had become quite ambitious. Von Neumann and Morgenstern wanted to reduce economics to a more manageable size (see p. 7 in [4]):

It seems to us that the same standard of modesty should be applied in economics. It is futile to try to explain – and ‘systematically’ at that – everything economic. The sound procedure is to obtain first utmost precision and mastery in a limited field, and then to proceed to another, somewhat wider one, and so on. This would also do away with the unhealthy practice of applying so-called theories to economic or social reform where they are in no way useful. We believe that it is necessary to know as much as possible about the behavior of the individual and about the simplest forms of exchange.

Applying game theory to innovation isn’t new – people thought of doing so in the 1960s, but this approach has become more common in recent years [3]. Game theory is applicable to any aspect of innovation in which two or more agents are in the same situation, including both competition (also called noncooperation) or cooperation. When it comes to innovation, obviously a “game” approach can apply to various phases, including:

• Pre-innovation funding
• Innovation creation
• Post-innovation funding
• Innovation IP protection
• Innovation production
• Innovation promotion

The strength of game theory as a way of understanding innovation is that it emphasizes decisions based on changing conditions caused by interactions among two or more agents. This captures the dynamic nature of innovation and the system in which innovation occurs. Theoretically speaking, there is no limit to the number of agents that may be interacting. This is clear from the very title of a notable paper by Wen Zhou et al., “An Extended N-Player Network Game and Simulation of Four Investment Strategies on a Complex Innovation Network” [5].

Systems Theory: Innovation as a Complex Adaptive System

In some cases, innovation occurs in relation not so much to individual players but rather to systems. And innovation itself can be seen as a complex adaptive system, which is any system made up of many discrete agents (digital bits, grains of sand, microbes, roots, insects or people – any entities) that interact in such a way that new systems emerge from their interactions [6].

As such, innovators should be aware of their surrounding organizations as complex adaptive systems. This is the implication of research by da Silva and Guerrini on innovation in complex systems, which casts doubt on the efficacy of hierarchical systems for innovation [7, 8].

In fact, innovation itself can be seen as a complex system. This is the important conclusion of a paper by Muller et al., that the “complexity of innovation systems arises from interaction between actors, existence of feed-back loops, and non-linearity of processes.” Those involved in innovation – with its multiplicity of contributors, its propensity to both counter and propagate and its general chaos – can readily recognize their world [9]. A recent doctoral dissertation by Vindeløv-Lidzélius concludes that by “bringing different facets of complexity and social constructionism together, innovation may be understood as the emergence of new meaning in dissipative structures” [10]. 

Companies need to become more aware of how the individual brains, groups/teams, organizations and the economy (market realities surrounding the individuals, groups and organizations) interact during innovation (as shown in Figure 1).

Figure 1 helps the reader conceptualize innovation as a complex adaptive system, as an idea can travel from the human brain to a team, to an organization, to markets. Conversely, markets can affect organizations, teams and individuals. The cohesiveness of this picture goes a long way to explaining the zeitgeist that enables simultaneous inventions. How can companies use models such as game theory or systems theory to overcome obstacles to the creation and diffusion of inventions? The key to both lies in situational awareness, including awareness of threats to survival.

Creating True Innovation

Blocks to initial creation can be overcome if the inventor has a realistic view of the situation giving rise to the invention and the situation surrounding its diffusion. Consider a company that designs and manufactures knives, spoons and forks the same way with the same materials, always marketing to the same population, year in and year out, without paying any attention to the changing reality around it –demographic trends, consumer preferences, dietary trends, supply chain issues, price of raw materials – all the issues normally covered in analyses for this industry [11]. If life itself were not so full of changes, this company could succeed. But the problem is that the state of the world is highly dynamic, making such a company vulnerable to failure. To survive, the company must change with the times. So, the company adjusts to changing circumstances – changing design, producing more or less of a certain implement, changing suppliers and so forth.

But none of this can be called “innovation.” True innovation means creating something both useful and new. This means also understanding exactly where the inventor is in place and time to the broadest situation of all: where we are in human history. Innovation involves being on the lookout for larger-than-life paradigm shifts, as described in Thomas Kuhn’s 1970 book on the structure of scientific revolution. According to Kuhn, “crisis simultaneously loosens the stereotypes and provides the incremental data necessary for a fundamental paradigm shift” [12]. Kuhn’s history of science traced paradigm shifts in physics from dominant models of Copernicus to Newton to Einstein – all occurring in breakthrough fashion, rather than slowly. In a recent article, Larry Clark of Harvard Business Publishing opined that crises generate innovations for four reasons: Crises inspire unity around a common purpose, grant new perspectives, unfreeze organizations and motivate action [13].

This review of game theory and complex adaptive systems illustrates the importance of understanding the nature and context of innovation. We learn from these theories that we can view innovation as a process of responding to a complex environment. This leads us directly to the model most well known for dealing with situations of conflict – war. Our next installment will consider the famous framework coming out on top in a high-stakes conflict known as the OODA loop.

References and Notes

1. A situation can occur in which no player has any incentive to change a strategy. This is called the Nash equilibrium, named after Princeton mathematician John Nash, and is one of the most well-known theories; every conceivable game is said to have a “Nash equilibrium.”
2. List of games in game theory – Wikipedia
3. Baniak, A. and Dubina, I., 2012, “Innovation Analysis and Game Theory: A Review,” Innovation: Organization & Management, Vol. 14, No. 2, pp. 178-191.
4. John von Neumann and Oskar Morgenstern, 1953, “The Theory of Games and Economic Behavior,” Princeton, N.J.: Princeton University Press.
5. Zhou, W., Koptyug, N., Ye, S., Jia, Y. and Lu, X., 2016, “An Extended N-Player Network Game and Simulation of Four Investment Strategies on a Complex Innovation Network,” PLoS ONE, Vol. 11, No. 1, Art. no. e0145407, https://doi.org/10.1371/journal.pone.0145407.
6. Sullivan, T., 2011, “Embracing Complexity,” Harvard Business Review, September, https://hbr.org/2011/09/embracing-complexity.
7. Da Silva, A.L. and Guerrini, F.M., 2018, “Self-organized innovation networks from the perspective of complex systems: A comprehensive conceptual review,” Journal of Organizational Change Management, Vol. 31, No. 5, 1108/JOCM-10-2016-0210.
8. This study [7] reviewed articles between 2000 and 2014 on one or more of the following topics: innovation networks, complex systems, self-organization and self-organizing. Based on the abstract, the authors conclude by questioning “the classical form of organizational management in innovation networks, essentially based on the concentration of hierarchical power.”
9. Muller, E., Héraud, J.-A. and Zenker, A., n.d., “Are innovation systems complex systems?,” http://evoreg.eu/docs/files/shno/Note_evoREG_33.pdf.
10. Vindeløv-Lidzélius, B.C., 2018, “Innovation in Complex Systems,” Doctoral dissertation, Tilburg University, https://research.tilburguniversity.edu/en/publications/innovation-in-complex-systems-an-exploration-in-strategy-leadersh.
11. See “Global Cutlery Industry (2020 to 2025) – North America will hold a Considerable Market Share – ResearchAndMarkets.com”
12. Thomas S. Kuhn, 1970, “The Structure of Scientific Revolutions,” 2nd ed., Chicago: University of Chicago Press, p. 89.
13. Larry Clark, 2020, “Innovation in a Time of Crisis,” Harvard Business Publishing, March 26, https://www.harvardbusiness.org/innovation-in-a-time-of-crisis/.