Cooperation, Norms, and Revolutions: A Unified Game-Theoretical Approach

Cooperation is of utmost importance to society as a whole, but is often challenged by individual self-interests. While game theory has studied this problem extensively, there is little work on interactions within and across groups with different preferences or beliefs. Yet, people from different social or cultural backgrounds often meet and interact. This can yield conflict, since behavior that is considered cooperative by one population might be perceived as non-cooperative from the viewpoint of another. To understand the dynamics and outcome of the competitive interactions within and between groups, we study game-dynamical replicator equations for multiple populations with incompatible interests and different power (be this due to different population sizes, material resources, social capital, or other factors). These equations allow us to address various important questions: For example, can cooperation in the prisoner's dilemma be promoted, when two interacting groups have different preferences? Under what conditions can costly punishment, or other mechanisms, foster the evolution of norms? When does cooperation fail, leading to antagonistic behavior, conflict, or even revolutions? And what incentives are needed to reach peaceful agreements between groups with conflicting interests? Our detailed quantitative analysis reveals a large variety of interesting results, which are relevant for society, law and economics, and have implications for the evolution of language and culture as well

A new era for the South African Journal of Sports Medicine

A new era for the South African Journal of Sports Medicin

Predicting Individuals' Learning Success from Patterns of Pre-Learning MRI Activity

Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the benefit that a person might reap from training? Several behavioral measures have been used to predict individual differences in task improvement, but their predictive power is limited. Here we show that individual differences in patterns of time-averaged T2*-weighted MRI images in the dorsal striatum recorded at the initial stage of training predict subsequent learning success in a complex video game with high accuracy. These predictions explained more than half of the variance in learning success among individuals, suggesting that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task. Surprisingly, predictions from white matter were highly accurate, while voxels in the gray matter of the dorsal striatum did not contain any information about future training success. Prediction accuracy was higher in the anterior than the posterior half of the dorsal striatum. The link between trainability and the time-averaged T2*-weighted signal in the dorsal striatum reaffirms the role of this part of the basal ganglia in learning and executive functions, such as task-switching and task coordination processes. The ability to predict who will benefit from training by using neuroimaging data collected in the early training phase may have far-reaching implications for the assessment of candidates for specific training programs as well as the study of populations that show deficiencies in learning new skills

Ecological commonalities among pelagic fishes: comparison of freshwater ciscoes and marine herring and sprat

Systematic comparisons of the ecology between functionally similar fish species from freshwater and marine aquatic systems are surprisingly rare. Here, we discuss commonalities and differences in evolutionary history, population genetics, reproduction and life history, ecological interactions, behavioural ecology and physiological ecology of temperate and Arctic freshwater coregonids (vendace and ciscoes, Coregonus spp.) and marine clupeids (herring, Clupea harengus, and sprat, Sprattus sprattus). We further elucidate potential effects of climate warming on these groups of fish based on the ecological features of coregonids and clupeids documented in the previous parts of the review. These freshwater and marine fishes share a surprisingly high number of similarities. Both groups are relatively short-lived, pelagic planktivorous fishes. The genetic differentiation of local populations is weak and seems to be in part correlated to an astonishing variability of spawning times. The discrete thermal window of each species influences habitat use, diel vertical migrations and supposedly also life history variations. Complex life cycles and preference for cool or cold water make all species vulnerable to the effects of global warming. It is suggested that future research on the functional interdependence between spawning time, life history characteristics, thermal windows and genetic differentiation may profit from a systematic comparison of the patterns found in either coregonids or clupeids

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Reversals of fortune: path dependency, problem solving, and temporal cases

Historical reversals highlight a basic methodological problem: is it possible to treat two successive periods both as independent cases to compare for causal analysis and as parts of a single historical sequence? I argue that one strategy for doing so, using models of path dependency, imposes serious limits on explanation. An alternative model which treats successive periods as contrasting solutions for recurrent problems offers two advantages. First, it more effectively combines analytical comparisons of different periods with narratives of causal sequences spanning two or more periods. Second, it better integrates scholarly accounts of historical reversals with actors’ own narratives of the past