Christina Fang

Balancing Exploration and Exploitation Through Structural Design: The Isolation of Subgroups and Organizational Learning

The classic trade-off between exploration and exploitation in organizational learning has attracted vigorous attention by researchers over the last two decades. Despite this attention, however, the question of how firms can better maintain the balance of exploration and exploitation remains unresolved. Drawing on a wide range of research on population and organization structure, we argue that an organization divided into semi-isolated subgroups may help strike this balance. We simulate such an organization, systematically varying the interaction pattern between individuals to explore how the degree of subgroup isolation and intergroup connectivity influences organizational learning. We also test this model with a range of contingency variables highlighted in the management research. We find that moderate levels of cross-group linking lead to the highest equilibrium performance by enabling superior ideas to diffuse across groups without reducing organizational diversity too quickly. This finding is remarkably resilient to a wide range of variance in factors such as problem complexity, environmental dynamism, and personnel turnover.

Near-Term Liability of Exploitation: Exploration and Exploitation in Multistage Problems

The classic trade-off between exploration and exploitation reflects the tension between gaining new information about alternatives to improve future returns and using the information currently available to improve present returns. By considering these issues in the context of a multistage, as opposed to a repeated, problem environment, we show that exploratory behavior has value quite apart from its role in revising beliefs. We show that even if current beliefs provide an unbiased characterization of the problem environment, maximizing with respect to these beliefs may lead to an inferior expected payoff relative to other mechanisms that make less aggressive use of the organizations beliefs. Search can lead to more robust actions in multistage decision problems than maximization, a benefit quite apart from its role in the updating of beliefs.

On Adaptive Emergence of Trust Behavior in the Game of Stag Hunt

We study the emergence of trust behavior at both the individual and the population levels. At the individual level, in contrast to prior research that views trust as fixed traits, we model the emergence of trust or cooperation as a result of trial and error learning by a computer algorithm borrowed from the field of artificial intelligence (Watkins 1989). We show that trust can indeed arise as a result of trial and error learning. Emergence of trust at the population level is modeled by a grid-world consisting of cells of individual agents, a technique known as spatialization in evolutionary game theory. We show that, under a wide range of assumptions, trusting individuals tend to take over the population and trust becomes a systematic property. At both individual and population levels, therefore, we argue that trust behaviors will often emerge as a result of learning.

Predicting the Next Big Thing: Success as a Signal of Poor Judgment

Successfully predicting that something will become a big hit seems impressive. Managers and entrepreneurs who have made successful predictions and have invested money on this basis are promoted, become rich, and may end up on the cover of business magazines. In this paper, we show that an accurate prediction about such an extreme event, e.g., a big hit, may in fact be an indication of poor rather than good forecasting ability. We first demonstrate how this conclusion can be derived from a formal model of forecasting. We then illustrate that the basic result is consistent with data from two lab experiments as well as field data on professional forecasts from the Wall Street Journal Survey of Economic Forecasts.

Perspective-Chance Explanations in the Management Sciences

We propose that random variation should be considered one of the most important explanatory mechanisms in the management sciences. There are good theoretical reasons to expect that chance events strongly impact organizational behavior and outcomes. We argue that models built on random variation can provide parsimonious explanations of several important empirical regularities in strategic management and organizational behavior. The reason is that random variation in a structured system can give rise to systematic patterns at the macro level. Here, we define the concept of a chance explanation; describe the theoretical mechanisms by which random variation generates patterns at the macro level; outline how key empirical regularities in management can be explained by chance models; and discuss the implications of chance models for theoretical integration, empirical testing, and management practice.

Organizational Learning as Credit Assignment: A Model and Two Experiments

We outline a theoretical model of organizational learning curves to account for the empirical regularities observed in the literature. The learning mechanism in our model is the gradual recognition of important stepping stones to achieving the goal. As organizations gain experience, they discover the appropriate actions to take at each stage and reduce the number of the steps it takes to reach the final outcome. Using both simulation and human subject experiments, we show that this model accounts for existing empirical regularities related to the learning curves, variations in learning rates, and organizational adaptation to new environments.

Strategizing with Biases: Making Better Decisions Using the Mindspace Approach:

This article introduces strategists to the Mindspace framework and explores its applications in strategic contexts. This framework consists of nine effective behavioral interventions that are grounded in public policy applications, and it focuses on how changing the context can be more effective than attempts to de-bias decision makers. Behavioral changes are likely when we follow rather than fight human nature. Better decisions can be achieved by engineering choice contexts to “engage a bias” to overcome a more damaging bias. This article illustrates how to engineer strategic contexts through two case studies and outlines directions and challenges when applying Mindspace to strategic decisions.