Meaningful learning in management: recombining strands of knowledge DNA through engaged dialog and generative conflict

This paper explores how meaningful learning objectives in management classes are pursued when the focus is on classroom activities and strategies that foster transformative thought, adaptive growth, and commitment from both instructors and students to achieve meaningful learning. To this end, we offer a metaphor and a context for this approach to learning. The DNA of learning metaphor details effective pedagogical practices and encourages instructors to take a more challenging and possibly transformative approach to their course design and classroom experiences

A Study on the Parallelization of Terrain-Covering Ant Robots Simulations

Agent-based simulation is used as a tool for supporting (time-critical) decision making in differentiated contexts. Hence, techniques for speeding up the execution of agent-based models, such as Parallel Discrete Event Simulation (PDES), are of great relevance/benefit. On the other hand, parallelism entails that the final output provided by the simulator should closely match the one provided by a traditional sequential run. This is not obvious given that, for performance and efficiency reasons, parallel simulation engines do not allow the evaluation of global predicates on the simulation model evolution with arbitrary time-granularity along the simulation time-Axis. In this article we present a study on the effects of parallelization of agent-based simulations, focusing on complementary aspects such as performance and reliability of the provided simulation output. We target Terrain Covering Ant Robots (TCAR) simulations, which are useful in rescue scenarios to determine how many agents (i.e., robots) should be used to completely explore a certain terrain for possible victims within a given time. © 2014 Springer-Verlag Berlin Heidelberg

Game Theoretical Interactions of Moving Agents

Game theory has been one of the most successful quantitative concepts to describe social interactions, their strategical aspects, and outcomes. Among the payoff matrix quantifying the result of a social interaction, the interaction conditions have been varied, such as the number of repeated interactions, the number of interaction partners, the possibility to punish defective behavior etc. While an extension to spatial interactions has been considered early on such as in the "game of life", recent studies have focussed on effects of the structure of social interaction networks. However, the possibility of individuals to move and, thereby, evade areas with a high level of defection, and to seek areas with a high level of cooperation, has not been fully explored so far. This contribution presents a model combining game theoretical interactions with success-driven motion in space, and studies the consequences that this may have for the degree of cooperation and the spatio-temporal dynamics in the population. It is demonstrated that the combination of game theoretical interactions with motion gives rise to many self-organized behavioral patterns on an aggregate level, which can explain a variety of empirically observed social behaviors

Integrating Assertive Community Treatment and Illness Management and Recovery for Consumers with Severe Mental Illness

This study examined the integration of two evidence-based practices for adults with severe mental illness: Assertive community treatment (ACT) and illness management and recovery (IMR) with peer specialists as IMR practitioners. Two of four ACT teams were randomly assigned to implement IMR. Over 2 years, the ACT–IMR teams achieved moderate fidelity to the IMR model, but low penetration rates: 47 (25.7%) consumers participated in any IMR sessions and 7 (3.8%) completed the program during the study period. Overall, there were no differences in consumer outcomes at the ACT team level; however, consumers exposed to IMR showed reduced hospital use over time

Local Convergence and Global Diversity: From Interpersonal to Social Influence

Axelrod (1997) showed how local convergence in cultural influence can preserve cultural diversity. We argue that central implications of Axelrod's model may change profoundly, if his model is integrated with the assumption of social influence as assumed by an earlier generation of modelers. Axelrod and all follow up studies employed instead the assumption that influence is interpersonal (dyadic). We show how the combination of social influence with homophily allows solving two important problems. Our integration of social influence yields monoculture in small societies and diversity increasing in population size, consistently with empirical evidence but contrary to earlier models. The second problem was identified by Klemm et al.(2003a,b), an extremely narrow window of noise levels in which diversity with local convergence can be obtained at all. Our model with social influence generates stable diversity with local convergence across a much broader interval of noise levels than models based on interpersonal influence.Comment: 20 pages, 3 figures, Paper presented at American Sociological Association 103rd Annual Meeting, August 1-4, 2008, Boston, MA. Session on Mathematical Sociolog

Learning and innovative elements of strategy adoption rules expand cooperative network topologies

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3 Tables, 12 Figures and 116 reference

Impact of Intimate Partner Violence on Parenting and Children’s Externalizing Behaviors: Transactional Processes Over Time

This study used longitudinal data to examine the transactional associations between mothers’ spanking and mother–child relationship quality with children’s externalizing behaviors in the context of intimate partner violence (IPV). Data came from a sample of 1,152 low-income mothers with children age 10–14 years. Results showed that past-year IPV triggered transactional associations by increasing children’s externalizing behaviors which, in turn, increased spanking and subsequently more externalizing behaviors. Transactional associations were also found for relationship quality. All outcomes used were mothers-reported except relationship quality. Implications for practice include the importance of the mother–child dyad and their reciprocal processes in assessment and treatment