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This paper studies how innovation teams can be optimally configured to yield the best possible performance at different stages of a certain technology's life cycle, which correspond to different levels of environmental complexity. To conduct our analysis, we have employed computational simulations of communities searching NK landscapes at varying levels of complexity. We studied how the relative proportion of exploring agents to exploiting agents in a community impacts the evolution of scores over time, and conducted additional investigations into the role of specialization (i.e., the agents' propensity to take their preferred action) and density (i.e., the expected width of social groups within the community). We discover that majority‐explorer teams are to be preferred when complexity is high and over the long run, whereas majority‐exploiter teams are more effective in the short run and at low complexities. Furthermore, we show that higher levels of specialization yield better results at higher complexities, and that majority‐explorer teams benefit the most from higher levels of density. We conclude that different team compositions are to be preferred at different stages of maturity, and that selecting a time horizon for operations is of crucial importance when designing an innovation team. [ABSTRACT FROM AUTHOR]

This paper introduces a new scheme for autonomous electricity cooperatives, called predictive cooperative, which aggregates commercial and residential electricity consumers and participates in the electricity market on behalf of its members. An axiomatic approach is proposed to calculate the day-ahead bid and to disaggregate the collective cost among participating consumers. The resulting formulation is shown to keep the members incentivized to both participate in the cooperative and remain truthful in reporting their expected loads. The scheme is implemented using PJM (world’s largest wholesale electricity market) real-time and day-ahead price data for 2015 and a collection of residential and commercial load profiles. The model performance of this framework is compared to that of the real-time pricing scheme, in which wholesale market prices are directly applied to individual consumers. The results show truthful load announcement by consumers, reduction in electricity price variation for all consumers, and comparative benefits for participants. [ABSTRACT FROM PUBLISHER]

Effective use of modularity in distributed systems is key to accommodating the complexity arising from having diverse requirements and stakeholders and increased environmental uncertainties. Moreover, modularity facilitates efficient dynamic resource allocation in the system. In this paper, a formal framework that links various classes of systems modularity to distributed decision complexities is presented by formulating modularity as an emergent phenomenon arising from environmental heterogeneity. This paper also demonstrates behavioral and mathematical formulations of the problem and agent-based simulation of sample networked systems to verify the results. [ABSTRACT FROM PUBLISHER]

Cooperation and competition are critical aspects of complex systems that intrigue researchers and practitioners alike. Recently, there has been great interest in how structure affects these cooperative behaviors and how they evolve. In this paper, we utilize the methods of evolutionary game theory on graphs to develop a model of the structured evolution of adaptive agents. Rather than simple memoryless agents, we employ adaptive agents that take on a diverse set of strategies including the cooperative catalyst tit-for-tat (TFT) and its generous and suspicious variants. We also employ all-defect, unconditional cooperation, and random strategies. Agents use these strategies during their evolution in order to maximize their own relative fitness, measured by their performance in a repeated Prisoner's Dilemma game versus their structural neighbors. We test the model with a variety of regular graph structures with variable degree, randomness, and size. Our parallel-execution agent-based simulations show that each strategy evolves toward its own structural niche with suspicious TFT maintaining the highest survival rate over all structures. We also show that increases in network density make the TFT strategies even more dominant, and connectivity randomness encourages cooperation in sparse networks. [ABSTRACT FROM PUBLISHER]