The Parable of the Hare and the Tortoise: How "Small Worlds" Reduce the Long Run Performance of Systems David Lazer Program on Networked Governance Harvard University

Acknowledgements… Allan Friedman NSF grant

Living in the (self-consciously) networked age Growth of research on networks across disciplines We live in an “smaller world” with ever- accelerating flows of information Explosion of consultants, software, etc to make organizations “smaller”

Does connecting people help an organization solve problems?

The problem of parallel problem solving in human systems Many agents working on same problem simultaneously How is that problem solving aggregated?

Brainstorming

“Laboratories of democracy”

Global diffusion…

(Not) Re-inventing the wheel

Roadmap The role of informational diversity in systemic performance Networks as architecture for experimentation Description of model Results Conclusion

Role of informational diversity Sunstein, Nemeth, etc.  Informational diversity provides the menu of options in the system However: pressures toward homogeneity, some of which may increase system performance (e.g., the elimination of bad solutions)

Processes of emulation Neo-institutionalism– strong pressures for conformity (DiMaggio and Powell) Networks play a key conduit for those pressures (Lazarsfeld, Friedkin, Lazer) Convergence often not on system “optimum”, even when emulation is driven by success (Bikhchandani, Hirshleifer, and Welch; Strang and Macy)

Network structure Cliquish Small world– “six degrees of separation” (Milgram, Watts) Birds of a feather (Lazarsfeld and Merton) “Scale free” (Barabasi)  how does the architecture of the network affect balance between exploration and exploitation?

Cliques

Small worlds (Milgram, Watts and Strogatts) Big worldSmall world

Birds of a feather…

Scale Free networks (Barabasi)

Network structure Cliquish Small world– “six degrees of separation” (Milgram, Watts) Birds of a feather (Lazarsfeld and Merton) “Scale free” (Barabasi)  how does the architecture of the network affect balance between exploration and exploitation?

Computational model KISS principle– simplest possible model that captures some essence of reality Agent-based– decision rules dictating agent behavior based on local conditions (not analytically tractable) “Experimentally” manipulate parameters, test for robustness Key question: what systemic patterns emerge?

Model Problem space– what’s the problem agents are trying to solve? Agent decision rules– how do agents seek improvements in performance? Agent neighborhood– who do agents see (and emulate)?

Problem space Key attribute of problem space is its ruggedness

Easy to find optimum…

Less easy to find optimum…

Problem space NK model (Kauffman) N dimensions (19 in these simulations) The marginal contribution of each dimension to performance is contingent on K other dimensions K determines the ruggedness of the problem space (5 in most of these simulations) Scores are calculated using a rank-preserving monotonic transformation

Decision rule Capacity of agents to search problem space must be very limited

Decision rule If someone agent can see is doing better than agent at time t, copy best alternative. Otherwise, look at impact of randomly changing one dimension. If this is an improvement, move there. If not an improvement, stay at previous solution.

Informational velocity Always looking at others? If not: –Is communication synchronous (e.g., group meetings)? –Is communication asynchronous?

Network– determines neighborhood Linear (max degrees of separation = population size – 1) Fully connected (max degrees of separation = 1)

Basic model parameters 100 agents 200 time steps 1000 simulations of each experiment –20 NK spaces (N = 19, K = 5) –50 randomly seeded starting points Vary size, network structure, velocity, and synchronicity Code written in Java using the Repast libraries

Findings Size Network structure Velocity Synchronicity

Bigger is better

The hare and the tortoise: Small worlds are good for a quick fix…

…but not so good in the long haul

Small worlds drive out variety

LR Performance of random graphs

Small worlds

Impact of structure is contingent on problem space

Velocity increases exploitation and decreases exploration

Synchronicity.

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Heterogeneity

The Social Structure of Exploration and Exploitation (March 1991) Exploration– looking for new solutions (experimentation) Exploitation– taking advantage of what the system knows (emulation)

Illustrations Agricultural diffusion Creative groups

Technological diffusion Diamond, Guns, Germs, and Steel “…[G]eographic connectedness has exerted both positive and negative effects on the evolution of technology. As a result, in the very long run, technology may have developed most rapidly in regions with moderate connectedness, neither too high nor too low. Technology’s course over the last 1,000 years in China, Europe, and possibly the Indian subcontinent exemplifies those net effects of high, moderate, and low connectedness, respectively.” (p. 416)

Creative groups Field work on creative groups suggests curvilinear relationship between performance and connectedness (Leenders) Experimental work on problem solving groups (Goldstone) Broadway (Uzzi and Spiro) Project teams (Binz-Scharf)

Conclusions Trade-off between networks that perform well in the short run vs long run –Small, high bandwidth, worlds good for SR, bad in LR Tragedy of the network: Trade-off between interests of individuals and system Are some networks better than others in both SR and LR? Are some networks good “compromises”?

Extensions Vary problem space Error in copying (crossover) Timing of “velocity” Assume some heterogeneity in problem space Make network endogenous Have landscape change

Genetic programming Holland, Koza, solution “breeding” Performs much better if there are multiple (largely) isolated populations, within which there is great intermixing and competition, between which there is little (> 0) Speciation