Learning in Complex Networks Christina Fang New York University Jeho Lee Korea Advanced Institute of Science and Technology Melissa A. Schilling New York University

Exploration versus Exploitation in Learning One key way that individuals learn is through sharing ideas with each other Individuals that rapidly identify and adopt higher performing ideas from others learn efficiently However, too rapid diffusion of higher performing beliefs through a population eliminates variety  lower organizational performance in long run (March, 1991).

Learning Rates and Interpersonal Structure Learning Rates (March, 1991) “Fast learning” results in convergence on common set of ideas “Slow learning” permits more exploration and higher long-term performance Interpersonal Structure Interpersonal structure also influences speed of idea diffusion  speed of convergence Some amount of isolation may be beneficial

Assumptions and Research Questions Key Assumption of Our Work We extend March’s model by assuming that individuals learn from other individuals. Questions How does organizational structure influence organizational learning? Is there any way to improve the balance between learning speed and performance?

Interpersonal Network Structure a) Nearly-isolated subgroup structure b) Semi-isolated subgroup structure with randomly rewired links c) Random network: Network structure without subgroup identity Begin with a sparse & clustered network, and systematically vary rewiring rate 

Simulation Model Individuals initially join organization with randomly generated m-dimensional belief sets Individuals learn probabilistically from others to which they are directly linked Payoff function with tunable parameter s (an increase in s makes the search problem more interdependent and difficult ) :  (x) = where δ j = 1 if jth belief for an individual corresponds with reality on that dimension, δ j = 0 otherwise.

Simulation Parameters Basic Model: Individuals (n) = 280 Initial number of nearest neighbors (k) = 10 Dimensions (m) = 100 Learning rate (p) = 0.3 Sensitivity analyses run with varying levels of s, n, k and p.

Learning Performance: Equilibrium Outcomes Organizational performance highest when subgroups are “semi-isolated” with a modest fraction of cross-group links. Is lower when subgroups are “completely” or “nearly isolated.” Is lower when subgroups do not exist. Fully connected Too many cross-group links

Graph of Results Semi-isolated No Sub-group Identity Nearly-isolated

Diversity of Belief Sets Diversity is lost very quickly in fully connected networks or networks with many cross-group links. Diversity is lost very slowly in nearly isolated networks. Diversity is lost at a moderate pace in the semi- isolated subgroup structure.

Dissimilarity of Belief Sets over Time Semi-isolated No Sub-group Identity Nearly-isolated

Dissimilarity of Belief Sets over Time (without Beta = 0) Semi-isolated No Sub-group Identity

Key Findings Localized subpopulations (subgroup structure) preserve heterogeneous knowledge, and foster requisite variety for exploration (akin to “genetic drift” – Wright, 1932) However, need a modest amount of cross-group links to reap the value of this heterogeneity Sensitivity analyses suggest a tradeoff (compensating effect) between learning rates and cross-group links at low levels of either parameter.

Implications, Limitations, and Future Directions Provides support for arguments that R&D groups should be moderately isolated from others, particularly for breakthrough innovations (Bower & Christensen, 1995) Simulations are highly stylized models of reality – will attempt to replicate findings in experimental setting, then field setting. “Majority rule” of simulation is crucial to outcomes – future research should explore effect of other types of decision rules.