Diversity and Communication in Teams: Improving Problem Solving or Creating Confusion?

Despite the rich and interdisciplinary debate on the role of diversity and communication in group problem solving, as well as the recognition of the interactions between the two issues, they have been rarely treated as a joint research topic. In this paper we offer a computational model of agents in teams and we assess the impact of various levels of diversity and communication on individual and collective performance at solving problems. By communication we intend a conversation on the persuasiveness of the features characterizing the problem setting. By diversity we mean differences in how agents build problem representations that allow them to access various solutions. We deploy the concept of diversity along two dimensions: knowledge amplitude, that is the relative amount of available knowledge with respect to the complete representation of a problem, and knowledge variety, that, for a given level of knowledge amplitude, regards differences in knowledge constituents Our results highlight the peculiar role and the interactions between the different sources of variety. Regarding knowledge amplitude, when agents have an incomplete representation of the problem, communication provides just confusion as it is difficult to find a common language for sharing thoughts, and agents perform better alone. Adding knowledge variety to this scenario, effects of communication are even more devastating. Conversely, as the representation of the problem gets more and more complete, communication becomes effective and displays a clear non-monotonic effect: after an optimal point, performance declines very rapidly and gets worse than the individual behavior. In this case, the introduction of knowledge variety further increases performance in teams, since benefits from integrating partial representations of the problem occur more frequently than communication clashes. Finally, highly diverse teams seem to be less sensitive to changes in communication strength, while as diversity declines, even small discrepancies from the optimal communication strength level might account for a strong variability of performance. In particular, overestimation of the required communication effort might cause severe performance breakdowns. Our results suggest that organizations and firms should jointly consider communication intensity and different sources of diversity in teams, since interactions among these variables might result in problem solving groups resembling more a Tower of Babel than an effective and helpful workplace.problem solving; diversity; heterogeneous agents; communication; constraint satisfaction; neural networks; causality

Diversity Communication in Teams: Improving Problem Solving or Creating Confusion?

Despite the rich and interdisciplinary debate on the role of diversity and communication in group problem solving, as well as the recognition of the interactions between the two topics, they have been rarely treated as a joint research issue. In this paper we develop a computational approach aimed at modeling problem solving agents and we assess the impact of various levels of diversity and communication in teams on agents' performance at solving problems. By communication we intend a conversation on the persuasiveness of the features characterizing the problem setting. By diversity we mean differences in how agents build problem representations that allow them to access various solutions. We deploy the concept of diversity along two dimensions: knowledge amplitude, that is, the amount of available knowledge (compared to the complete representation of a problem), and knowledge variety, which pertains to the differences in agents' knowledge endowments.x10Our results show the different impact of these two sources of variety on problem solving performance in teams, as well as their interplay. Regarding knowledge amplitude, when agents' representation of the problem is considerably incomplete, communication provides confusion as it is difficult to find a common language for sharing thoughts, and agents perform better alone. Adding knowledge variety to this scenario, the effects of communication are even more negative. Conversely, as the representation of the problem gets more and more complete, communication becomes more and more effective. Albeit displaying a clear non-monotonic effect: increasing the communication strength, performance increases until an optimal point, after which it declines and gets very rapidly worse than individual behavior. In this case, the introduction of knowledge variety further increases performance in teams, since benefits from integrating partial representations of the problem occur more frequently than communication clashes. Finally, highly diverse teams seem to be less sensitive to changes in communication strength, while as diversity declines, even small discrepancies from the optimal communication strength level might account for a strong variability of performance. In particular, overestimation of the required communication effort might cause severe performance breakdowns.x10Our results suggest that organizations and firms should jointly consider communication intensity and different sources of diversity in teams, since interactions among these variables might result in problem solving groups resembling more a Tower of Babel than an effective and helpful workplaceproblem solving; diversity; heterogeneous agents; communication; constraint; satisfaction; neural networks; causality

The long and winding road: Routine creation and replication in multi-site organizations

Prior research on organizational routines in the ‘capabilities’ literature has either studied how new routines are created during an exploratory process of variation and selection or how existing routines are replicated during a phase of exploitation. Few studies have analyzed the life cycle of new routine creation and replication as an integrated process. In an in-depth case study of England’s Highways Agency, this paper shows that the creation and replication of a new routine across multiple sites involves four sequential steps: envisioning, experimenting, entrenching and enacting. We contribute to the capabilities research in two ways: first, by showing how different organizational levels, capabilities and logics (cognitive and behavioural) shape the development of new routines; and second, by identifying how distinct evolutionary cycles of variation and selective retention occur during each step in the process. In contrast with prior research on replication as an exact copy of a template or existing routine, our study focuses on the replication of an entirely new routine (based on novel principles) that is adapted to fit local operational conditions during its large-scale replication across multiple sites. We draw upon insights from adjacent ‘practice research’ and suggest how capabilities and practice studies may complement each other in future research on the evolution of routines

Multi-robot team formation control in the GUARDIANS project

Purpose The GUARDIANS multi-robot team is to be deployed in a large warehouse in smoke. The team is to assist firefighters search the warehouse in the event or danger of a fire. The large dimensions of the environment together with development of smoke which drastically reduces visibility, represent major challenges for search and rescue operations. The GUARDIANS robots guide and accompany the firefighters on site whilst indicating possible obstacles and the locations of danger and maintaining communications links. Design/methodology/approach In order to fulfill the aforementioned tasks the robots need to exhibit certain behaviours. Among the basic behaviours are capabilities to stay together as a group, that is, generate a formation and navigate while keeping this formation. The control model used to generate these behaviours is based on the so-called social potential field framework, which we adapt to the specific tasks required for the GUARDIANS scenario. All tasks can be achieved without central control, and some of the behaviours can be performed without explicit communication between the robots. Findings The GUARDIANS environment requires flexible formations of the robot team: the formation has to adapt itself to the circumstances. Thus the application has forced us to redefine the concept of a formation. Using the graph-theoretic terminology, we can say that a formation may be stretched out as a path or be compact as a star or wheel. We have implemented the developed behaviours in simulation environments as well as on real ERA-MOBI robots commonly referred to as Erratics. We discuss advantages and shortcomings of our model, based on the simulations as well as on the implementation with a team of Erratics.</p

Gainsharing: A Critical Review and a Future Research Agenda

This paper provides a critical review of the extensive literature on gainsharing. It examines the reasons for the fast growth in these programs in recent years and the major prototypes used in the past. Different theoretical formulations making predictions about the behavioral consequences and conditions mediating the success of these programs are discussed and the supporting empirical evidence is examined. The large number of a theoretical case studies and practitioner reports or gainsharing are also summarized and integrated. The article concludes with a suggested research agenda for the future

Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model

Multi-agent geographical models integrate very large numbers of spatial interactions. In order to validate those models large amount of computing is necessary for their simulation and calibration. Here a new data processing chain including an automated calibration procedure is experimented on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimise three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. The model is thus a useful tool for further multiple applications on empirical historical situations

Promoting New Patterns in Household Energy Consumption with Pervasive Learning Games

Engaging computer games can be used to change energy consumption patterns in the home. PowerAgent is a pervasive game for Java-enabled mobile phones that is designed to influence everyday activities and use of electricity in the domestic setting. PowerAgent is connected to the household’s automatic electricity meter reading equipment via the cell network, and this setup makes it possible to use actual consumption data in the game. In this paper, we present a two-level model for cognitive and behavior learning, and we discuss the properties of PowerAgent in relation to the underlying situated learning, social learning, and persuasive technology components that we have included in the game

Complexity models in design

Complexity is a widely used term; it has many formal and informal meanings. Several formal models of complexity can be applied to designs and design processes. The aim of the paper is to examine the relation between complexity and design. This argument runs in two ways. First designing provides insights into how to respond to complex systems – how to manage, plan and control them. Second, the overwhelming complexity of many design projects lead us to examine how better understanding of complexity science can lead to improved designs and processes. This is the focus of this paper. We start with an outline of some observations on where complexity arises in design, followed by a brief discussion of the development of scientific and formal conceptions of complexity. We indicate how these can help in understanding design processes and improving designs