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

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

A Search for Very Low-mass Stars and Brown Dwarfs in the Young sigma Orionis Cluster

We present a CCD-based photometric survey covering 870 sq. arcmin in a young stellar cluster around the young multiple star sigma Orionis. Our survey limiting R, I, and Z magnitudes are 23.2, 21.8, and 21.0, respectively. From our colour-magnitude diagrams, we have selected 49 faint objects, which smoothly extrapolate the photometric sequence defined by more massive known members. Adopting the currently accepted age interval of 2-10 Myr for the Orion 1b association and considering recent evolutionary models, our objects may span a mass range from 0.1 down to 0.02 Msun, well within the substellar regime. Follow-up low-resolution optical spectroscopy (635-920 nm) for eight of our candidates (I=16-19.5) shows that they have spectral types M6-M8.5 which are consistent with the expectations for true members. Compared with their Pleiades counterparts of similar types, Halpha emission is generally stronger, while NaI and KI absorption lines appear weaker, as expected for lower surface gravities and younger ages. Additionally, TiO bands and in particular VO bands appear clearly enhanced in our candidate with the latest spectral type, SOri 45 (M8.5, I=19.5), compared to objects of similar types in older clusters and the field. We have estimated the mass of this candidate at only 0.020-0.040 Msun, hence it is one of the least massive brown dwarfs yet discovered. We also discuss in this paper the potential role of deuterium as a tracer of both substellar nature and age in very young clusters.Comment: Accepted for publication in ApJ Main Journal. 32 pages of text and tables + 9 pages of figures. Figures 3a and 3b (gif format) provided separatel

Making use of fuzzy cognitive maps in agent-based modeling

One of the main challenges in Agent-Based Modeling (ABM) is to model agents’ preferences and behavioral rules such that the knowledge and decision-making processes of real-life stakeholders will be reflected. To tackle this challenge, we demonstrate the potential use of a participatory method, Fuzzy Cognitive Mapping (FCM), that aggregates agents’ qualitative knowledge (i.e., knowledge co-production). In our proposed approach, the outcome of FCM would be a basis for designing agents’ preferences and behavioral rules in ABM. We apply this method to a social-ecological system of a farming community facing water scarcity

The Rationality of Prejudices

We model an -player repeated prisoner's dilemma in which players are given traits (e.g., height, age, wealth) which, we assume, affect their behavior. The relationship between traits and behavior is unknown to other players. We then analyze the performance of “prejudiced” strategies—strategies that draw inferences based on the observation of some or all of these traits, and extrapolate the inferred behavior to other carriers of these traits. Such prejudiced strategies have the advantage of learning rapidly, and hence of being well adapted to rapidly changing conditions that might result, for example, from high migration or birth rates. We find that they perform remarkably well, and even systematically outperform both Tit-For-Tat and ALLD when the population changes rapidly

A Retrospective Database Analysis of Neonatal Morbidities to Evaluate a Composite Endpoint for Use in Preterm Labor Clinical Trials

Objective To propose and assess a composite endpoint (CE) of neonatal benefit based on neonatal mortality and morbidities by gestational age (GA) for use in preterm labor clinical trials. Study Design A descriptive, retrospective analysis of the Medical University of South Carolina Perinatal Information System database was conducted. Neonatal morbidities were assessed for inclusion in the CE based on clinical significance/risk of childhood neurodevelopmental impairment, frequency, and association with GA in a mother– neonate linked cohort, comprising women with uncomplicated singleton pregnancies delivered at !24 weeks’ GA. Results Among 17,912 mother–neonate pairs, neonates were at a risk of numerous severe but infrequent morbidities. Clinically important, predominantly rare events were combined into a CE comprising neonatal mortality and morbidities, which decreased in frequency with increasing GA. The highest CE frequency occurred at \u3c31 weeks. High frequency of respiratory distress syndrome, bronchopulmonary dysplasia, and sepsis drove the CE. Median length of hospital stay was longer at all GAs in those with the CE compared with those without. Conclusions Descriptive epidemiological assessment and clinical input were used to develop a CE to measure neonatal benefit, comprising clinically meaningful outcomes. These empirical data and CE allowed trials investigating tocolytics to be sized appropriately

Anticipated resource utilization for injury versus non-injury pediatric visits to emergency departments

Background Childhood injuries are increasingly treated in emergency departments (EDs) but the relationship between injury severity and ED resource utilization has not been evaluated. The objective of this study was to compare resource utilization for pediatric injury-related ED visits across injury-severity levels and with non-injury visits, using standardized, validated scales. Methods A retrospective analysis of 2004-2008 ED visits from the Pediatric Emergency Care Applied Research Network Core Data Project. Maximum Abbreviated Injury Scale severity (MAIS) and Severity Classification System (SCS) scores were calculated and compared. MAIS and SCS are ordinal scales from 1 (minor injury) to 6, and 1 (low anticipated resource utilization) to 5, respectively. ED length of stay (LOS) and admission percentages were calculated as comparative proxy measures of resource utilization. Results There were 763,733 injury visits and 2,328,916 non-injury visits, most with SCS of 2 or 3. Of the injured patients, 59.2 % had an MAIS of 1. ED LOS and admission percentage increased with increasing MAIS from 1-5. LOS and admission percentage increased with increasing SCS in both samples. Median LOS was shorter for injured versus non-injured patients with SCS 3-5. Non-injured patients with SCS 2-5 were more likely admitted than injured patients. Most injured patients had an SCS 3 with an MAIS 1-2, or an SCS 2 with an MAIS 1, with no correlation between the two scales. Conclusion While admission rates and LOS increase with increasing AIS and SCS severity, these two classification schemas do not reliably correlate. Similarly, ED visit metrics differ between injured and non-injured patients in similar SCS categories. Although AIS and SCS both have value, these differences should be considered when using these schemas in research and quality improvement