an agent-based model of optimal exploitation

Using an agent-based simulation, we illustrate how goal-seeking behavior affects network formation, learning, and performance. Our organization has one manager, who decides where to invest financial capital; individual workers, who decide where to work and prefer projects with larger budgets; and projects, which vary in quality. Our manager discovers high-quality projects from interactions with workers and allocates more capital to high-quality projects. When given an opportunity, our workers move to bigger-budget projects. We let our manager vary in terms of how much she exploits what she learns and allow our workers vary in terms of how sensitive they are to differences in capital. Our results highlight a contingency which shapes how goal-seeking behavior affects learning. The contingency is network fragility. Fragile connections decay quickly when individuals are not working together, while robust relationships decay more slowly. When relationships are robust, exploitation by our manager leads to a dense organizational network, improving information quality, and performance. Decisions by self-interested individuals (our manager and our workers) produce a virtuous learning cycle. When relationships are fragile, exploitation by our manager produces a sparse network, reducing information quality, and undermining performance. When network connections are fragile, the manager must find the right balance of exploitation and exploration, a balance which limits the rate at which workers move from one project to the next, allowing the manager to exploit some of what she knows, without undermining the very network which allows for useful information to be obtained.publishersversionpublishe

Power, status, and learning in organizations

This paper reviews the scholarly literature on the effects of social hierarchy—differences in power and status among organizational actors—on collective learning in organizations and groups. We begin with the observation that theories of organization and group learning have tended to adopt a rational system model, a model that emphasizes goal-directed and cooperative interactions between and among actors who may differ in knowledge and expertise but are undifferentiated with respect to power and status. Our review of the theoretical and empirical literatures on power, status, and learning suggests that social hierarchy can complicate a rational system model of collective learning by disrupting three critical learning-related processes: anchoring on shared goals, risk taking and experimentation, and knowledge sharing. We also find evidence to suggest that the stifling effects of power and status differences on collective learning can be mitigated when advantaged actors are collectively oriented. Indeed, our review suggests that higher-ranking actors who use their power and status in more “socialized” ways can play critical roles in stimulating collective learning behavior. We conclude by articulating several promising directions for future research that were suggested by our review

Forgotten Third Parties: Analyzing the Contingent Association Between Unshared Third Parties, Knowledge Overlap, and Knowledge Transfer Relationships with Outsiders

Third parties play a prominent role in network-based explanations for successful knowledge transfer. Third parties can be either shared or unshared. Shared third parties signal insider status and have a predictable positive effect on knowledge transfer. Unshared third parties, however, signal outsider status and are believed to undermine knowledge transfer. Surprisingly, unshared third parties have been ignored in empirical analysis, and so we do not know if or how much unshared third parties contribute to the process. Using knowledge transfer data from an online technical forum, we illustrate how unshared third parties affect the rate at which individuals initiate and sustain knowledge transfer relationships. Empirical results indicate that unshared third parties undermine knowledge sharing, and they also indicate that the magnitude of the negative unshared-third-party effect declines the more unshared third parties overlap in what they know. Our results provide a more complete view of how third parties contribute to knowledge sharing. The results also advance our understanding of network-based dynamics defined more broadly. By documenting how knowledge overlap among unshared third parties moderates their negative influence, our results show when the benefits provided by third parties and by bridges (i.e., relationships with outsiders) will be opposed versus when both can be enjoyed

Knowledge Utilization, Coordination, and Team Performance

Considerable research has established the superior performance of teams on which team members utilize specialized knowledge and also develop transactive processes that promote coordination. Less is known, however, about the consequences for team performance when team members only possess one of the two productivity factors. We develop and test a framework highlighting the distinct challenges these teams will face. In particular, our results show that each productivity factor contributed significantly more to team performance when the other factor was present. And our findings also illustrate a potential failure mode for knowledge utilization. If team members could not coordinate their collective efforts, utilizing knowledge undermined team performance. Our framework outlines a similar risk for too much coordination, if team members cannot utilize their specialized knowledge and are asked to perform a task with a “rugged” performance landscape. We discuss the implications of our framework and results for theory and practice

Shared language in the team network-performance association: Reconciling conflicting views of the network centralization effect on team performance

We reconcile two conflicting views of the network centralization effect on team performance. In one view, a centralized network is problematic because it limits knowledge transfer, making it harder for team members to discover productive combinations of their know-how and expertise. In the alternative view, the limits on knowledge transfer encourage search and experimentation, leading to the discovery of more valuable ideas. We maintain the two sides are not opposed but reflect two distinct ways centralization can affect a team’s shared problem-solving framework. The shared framework in our research is a shared language. We contend that team network centralization affects both how quickly a shared language emerges and the performance implications of the shared language that develops. We analyze the performance of 77 teams working to identify abstract symbols for 15 trials. Teams work under network conditions that vary with respect to centralization. Results indicate that centralized teams take longer to develop a shared language, but centralized teams also create a shared language that is more beneficial for performance. The findings also indicate that the highest performing teams are assigned to networks that combine elements of a centralized and a decentralized network

Commitment, Learning, and Alliance Performance: A Formal Analysis Using an Agent-Based Network Formation Model

Current theoretical arguments highlight a dilemma faced by actors who either adopt a weak or strong commitment strategy for managing their alliances and partnerships. Actors who pursue a weak commitment strategy|i.e. immediately abandon current partners when a more pro table alternative is presented|are more likely to identify the most rewarding alliances. On the other hand, actors who enact a strong commitment approach are more likely to take advantage of whatever opportunities can be found in existing partnerships. Using agent-based modeling, we show that actors who adopt a moderate commitment strategy overcome this dilemma and outperform actors who adopt either weak or strong commitment approaches. We also show that avoiding this dilemma rests on experiencing a related tradeo : moderately-committed actors sacri ce short-term performance for the superior knowledge and information that allows them to eventually do better

Close encounters: Analyzing how social similarity and propinquity contribute to strong network connections.

Models of network formation emphasize the importance of social similarity and propinquity in producing strong interpersonal connections. The positive effect each factor can have on tie strength has been documented across a number of studies, and yet we know surprisingly very little about how the two factors combine to produce strong ties. Being in close proximity could either amplify or dampen the positive effect that social similarity can have on tie strength. Data on tie strength among teachers working in five public schools were analyzed to shed light on this theoretical question. The empirical results indicate that teachers who were similar in age were more likely to be connected by a strong tie, especially teachers for whom age similarity was more likely to be salient. Moreover, teachers who took breaks at the same time or who had classrooms on the same floor communicated more frequently and felt more emotionally attached. Among the public school teachers, propinquity amplified the positive effect that age similarity had on tie strength. The strongest network connections occurred among age-similar teachers who had classrooms on the same floor. The empirical results illustrate the value of considering how social similarity and propinquity contribute to strong ties independently and when combined with each other

Measuring personal networks and their relationship with scientific production

The analysis of social networks has remained a crucial and yet understudied aspect of the efforts to measure Triple Helix linkages. The Triple Helix model aims to explain, among other aspects of knowledge-based societies, ¿the current research system in its social context. This paper develops a novel approach to study the research system from the perspective of the individual, through the analysis of the relationships among researchers, and between them and other social actors. We develop a new set of techniques and show how they can be applied to the study of a specific case (a group of academics within a university department). We analyse their informal social networks and show how a relationship exists between the characteristics of an individual¿s network of social links and his or her research output

Preferences, Identity, and Competition: Predicting Tie Strength from Demographic Data

This research examines the combined influence of three causal mechanisms shaping interpersonal dynamics. Sharing a demographic characteristic has been shown to strengthen a relationship between people. The initial explanation for this positive effect emphasized the importance of individual preferences for social contact. Similar people share common interests, which makes them more attractive as exchange partners. More recent explanations have highlighted the important role of identification and competition. Identification increases the tendency for strong ties to develop among similar people, while competition reduces that tendency. I argue that tie strength is a function of all three mechanisms: a baseline level of interpersonal attraction, a positive identification effect, and a negative competition adjustment. Identification and competition vary with the number of people sharing the focal attribute to define a predictable association between sharing the focal attribute and the strength of a network connection. Predictions are tested with tenure and tie-strength data from a small research and development firm. Analysis indicates that having the same tenure has the predicted effect on communication frequency. The implications of the findings are discussed for homophily research in particular and demography research in general.homophily, organizational demography, identity, competition, diversity