On Adaptive Heuristics that Converge to Correlated Equilibrium

I study the path properties of adaptive heuristics that mimic the natural dynamics of play in a game and converge to the set of correlated equilibria. Despite their apparent differences, I show that these heuristics have an abstract representation as a sequence of probability distributions that satisfy a number of common properties. These properties arise due to the topological structure of the set of correlated equilibria. The characterizations that I obtain have useful applications in the study of the convergence of the heuristics.

Adaptive Heuristics That (Could) Fit: Information Search and Communication Patterns in an Online Forum of Investors Under Market Uncertainty

This article examines information-search heuristics and communication patterns in an online forum of investors during a period of market uncertainty. Global connections, real-time communication, and technological sophistication have created an unpredictable market environment. As such, investors try to deal with semantic, strategic, and operational uncertainty by following heuristics that reduce information redundancy. In this study, we have tried to find traces of cognitive communication heuristics in a large-scale data set including 8 years of online posts (2004–2012) for a forum of Italian investors. We identified various market volatility conditions on a daily basis to understand the influence of market uncertainty on cognitive and communication processes. We found that investors communicated more dynamically when the market was unstable, while they were more prone to anchor heuristic when market uncertainty was invariant. Furthermore, abnormal market trends triggered more availability-based communication patterns. We also found that expertise matters. This would suggest that online communities need intelligent, context-specific tools to support partner selection and stimulate nonredundant communication.

Reducing the Size of Combinatorial Optimization Problems Using the Operator Vaccine by Fuzzy Selector with Adaptive Heuristics

Nowadays, solving optimally combinatorial problems is an open problem. Determining the best arrangement of elements proves being a very complex task that becomes critical when the problem size increases. Researchers have proposed various algorithms for solving Combinatorial Optimization Problems (COPs) that take into account the scalability; however, issues are still presented with larger COPs concerning hardware limitations such as memory and CPU speed. It has been shown that the Reduce-Optimize-Expand (ROE) method can solve COPs faster with the same resources; in this methodology, the reduction step is the most important procedure since inappropriate reductions, applied to the problem, will produce suboptimal results on the subsequent stages. In this work, an algorithm to improve the reduction step is proposed. It is based on a fuzzy inference system to classify portions of the problem and remove them, allowing COPs solving algorithms to utilize better the hardware resources by dealing with smaller problem sizes, and the use of metadata and adaptive heuristics. The Travelling Salesman Problem has been used as a case of study; instances that range from 343 to 3056 cities were used to prove that the fuzzy logic approach produces a higher percentage of successful reductions.