A Theoretical Analysis of Cooperative Behavior in Multi-Agent Q-learning

A number of experimental studies have investigated whether cooperative behavior may emerge in multi-agent Q-learning. In some studies cooperative behavior did emerge, in others it did not. This report provides a theoretical analysis of this issue. The analysis focuses on multi-agent Q-learning in iterated prisoner’s dilemmas. It is shown that under certain assumptions cooperative behavior may emerge when multi-agent Q-learning is applied in an iterated prisoner’s dilemma. An important consequence of the analysis is that multi-agent Q-learning may result in non-Nash behavior. It is found experimentally that the theoretical results derived in this report are quite robust to violations of the underlying assumptions.Cooperation;Multi-Agent Q-Learning;Multi-Agent Reinforcement Learning;Nash Equilibrium;Prisoner’s Dilemma

A Mathematical Analysis of the Long-run Behavior of Genetic Algorithms for Social Modeling

We present a mathematical analysis of the long-run behavior of genetic algorithms that are used for modeling social phenomena. The analysis relies on commonly used mathematical techniques in evolutionary game theory. Assuming a positive but infinitely small mutation rate, we derive results that can be used to calculate the exact long-run behavior of a genetic algorithm. Using these results, the need to rely on computer simulations can be avoided. We also show that if the mutation rate is infinitely small the crossover rate has no effect on the long-run behavior of a genetic algorithm. To demonstrate the usefulness of our mathematical analysis, we replicate a well-known study by Axelrod in which a genetic algorithm is used to model the evolution of strategies in iterated prisoner’s dilemmas. The theoretically predicted long-run behavior of the genetic algorithm turns out to be in perfect agreement with the long-run behavior observed in computer simulations. Also, in line with our theoretically informed expectations, computer simulations indicate that the crossover rate has virtually no long-run effect. Some general new insights into the behavior of genetic algorithms in the prisoner’s dilemma context are provided as well.genetic algorithm;economics;evolutionary game theory;long-run behavior;social modeling

Some Comments on the Question Whether Co-Occurrence Data Should Be Normalized

In a recent article in JASIST, L. Leydesdorff and L. Vaughan (2006) asserted that raw cocitation data should be analyzed directly, without first applying a normalization such as the Pearson correlation. In this communication, it is argued that there is nothing wrong with the widely adopted practice of normalizing cocitation data. One of the arguments put forward by Leydesdorff and Vaughan turns out to depend crucially on incorrect multidimensional scaling maps that are due to an error in the PROXSCAL program in SPSS.multidimensional scaling;PROXSCAL;Pearson correlation;author cocitation analysis;co-occurrence data;normalization

Appropriate Similarity Measures for Author Cocitation Analysis

We provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis

Bibliometric Mapping of the Computational Intelligence Field

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation

Some Comments on Egghe’s Derivation of the Impact Factor Distribution

In a recent paper, Egghe [Egghe, L. (in press). Mathematical derivation of the impact factor distribution. Journal of Informetrics] provides a mathematical analysis of the rank-order distribution of journal impact factors. We point out that Egghe’s analysis relies on an unrealistic assumption, and we show that his analysis is not in agreement with empirical data.distribution;rank-order distribution;impact factor

A Taxonomy of Bibliometric Performance Indicators Based on the Property of Consistency

We propose a taxonomy of bibliometric indicators of scientific performance. The taxonomy relies on the property of consistency. The h-index is shown not to have this important property.h-index;consistency;bibliometric performance indicator;scientific performance;taxonomy

Some Comments on the Question Whether Co-occurrence Data Should Be Normalized

In a recent paper in the Journal of the American Society for Information Science and Technology, Leydesdorff and Vaughan assert that raw cocitation data should be analyzed directly, without first applying a normalization like the Pearson correlation. In this report, it is argued that there is nothing wrong with the widely adopted practice of normalizing cocitation data. One of the arguments put forward by Leydesdorff and Vaughan turns out to depend crucially on incorrect multidimensional scaling maps that are due to an error in the PROXSCAL program in SPSS.Multidimensional scaling;Author cocitation analysis;Co-occurrence data;Normalization;PROXSCAL;Pearson correlation

Citation-based clustering of publications using CitNetExplorer and VOSviewer

Merit, Expertise and Measuremen

Citation-based clustering of publications using CitNetExplorer and VOSviewer

Merit, Expertise and Measuremen