Our brain is a complex system of interconnected regions spontaneously organized into distinct networks. The integration of information between and within these networks is a continuous process that can be observed even when the brain is at rest, i.e. not engaged in any particular task. Moreover, such spontaneous dynamics show predictive value over individual cognitive profile and constitute a potential marker in neurological and psychiatric conditions, making its understanding of fundamental importance in modern neuroscience. Here we present a theoretical and mathematical model based on an extension of evolutionary game theory on networks (EGN), able to capture brain's interregional dynamics by balancing emulative and non-emulative attitudes among brain regions. This results in the net behavior of nodes composing resting-state networks identified using functional magnetic resonance imaging (fMRI), determining their moment-to-moment level of activation and inhibition as expressed by positive and negative shifts in BOLD fMRI signal. By spontaneously generating low-frequency oscillatory behaviors, the EGN model is able to mimic functional connectivity dynamics, approximate fMRI time series on the basis of initial subset of available data, as well as simulate the impact of network lesions and provide evidence of compensation mechanisms across networks. Results suggest evolutionary game theory on networks as a new potential framework for the understanding of human brain network dynamics.

Decision analysis and game theory have proved useful tools in various biodiversity conservation planning and modeling contexts. This paper shows how game theory may be used to inform group decisions in biodiversity conservation scenarios by modeling conflicts between stakeholders to identify Pareto-inefficient Nash equilibria. These are cases in which each agent pursuing individual self-interest leads to a worse outcome for all, relative to other feasible outcomes. Three case studies from biodiversity conservation contexts showing this feature are modeled to demonstrate how game-theoretical representation can inform group decision-making.

Conflict destabilizes social interactions and impedes cooperation at multiple scales of biological organization. Of fundamental interest are the causes of turbulent periods of conflict. We analyze conflict dynamics in an monkey society model system. We develop a technique, Inductive Game Theory, to extract directly from time-series data the decision-making strategies used by individuals and groups. This technique uses Monte Carlo simulation to test alternative causal models of conflict dynamics. We find individuals base their decision to fight on memory of social factors, not on short timescale ecological resource competition. Furthermore, the social assessments on which these decisions are based are triadic (self in relation to another pair of individuals), not pairwise. We show that this triadic decision making causes long conflict cascades and that there is a high population cost of the large fights associated with these cascades. These results suggest that individual agency has been over-emphasized in the social evolution of complex aggregates, and that pair-wise formalisms are inadequate. An appreciation of the empirical foundations of the collective dynamics of conflict is a crucial step towards its effective management.

The physics of cancer dormancy, the time between initial cancer treatment and re-emergence after a protracted period, is a puzzle. Cancer cells interact with host cells via complex, non-linear population dynamics, which can lead to very non-intuitive but perhaps deterministic and understandable progression dynamics of cancer and dormancy.

Animal toxins that are used to subdue prey and deter predators act as the key drivers in natural food chains and ecosystems. However, the predators of venomous animals may exploit feeding adaptation strategies to overcome toxins their prey produce. Much remains unknown about the genetic and molecular game process in the toxin-dominant food chain model. Here, we show an evolutionary strategy in different trophic levels of scorpion-eating amphibians, scorpions and insects, representing each predation relationship in habitats dominated by the paralytic toxins of scorpions. For scorpions preying on insects, we found that the scorpion α-toxins irreversibly activate the skeletal muscle sodium channel of their prey (insect, BgNaV1) through a membrane delivery mechanism and an efficient binding with the Asp/Lys-Tyr motif of BgNaV1. However, in the predatory game between frogs and scorpions, with a single point mutation (Lys to Glu) in this motif of the frog's skeletal muscle sodium channel (fNaV1.4), fNaV1.4 breaks this interaction and diminishes muscular toxicity to the frog; thus, frogs can regularly prey on scorpions without showing paralysis. Interestingly, this molecular strategy also has been employed by some other scorpion-eating amphibians, especially anurans. In contrast to these amphibians, the Asp/Lys-Tyr motifs are structurally and functionally conserved in other animals that do not prey on scorpions. Together, our findings elucidate the protein-protein interacting mechanism of a toxin-dominant predator-prey system, implying the evolutionary game theory at a molecular level.

Clinical Coaching Cards is a serious game for faculty development in which players take turns as Teacher and Coach to apply teaching techniques on game cards to identify new approaches to teaching in the clinical environment. The game employs active learning theory and coaching frameworks.

Despite mounting evidence for the strong role of genetics in the phenotypic manifestation of Autism Spectrum Disorder (ASD), the specific genes responsible for the variable forms of ASD remain undefined. ASD may be best explained by a combinatorial genetic model with varying epistatic interactions across many small effect mutations. Coalitional or cooperative game theory is a technique that studies the combined effects of groups of players, known as coalitions, seeking to identify players who tend to improve the performance--the relationship to a specific disease phenotype--of any coalition they join. This method has been previously shown to boost biologically informative signal in gene expression data but to-date has not been applied to the search for cooperative mutations among putative ASD genes. We describe our approach to highlight genes relevant to ASD using coalitional game theory on alteration data of 1,965 fully sequenced genomes from 756 multiplex families. Alterations were encoded into binary matrices for ASD (case) and unaffected (control) samples, indicating likely gene-disrupting, inherited mutations in altered genes. To determine individual gene contributions given an ASD phenotype, a "player" metric, referred to as the Shapley value, was calculated for each gene in the case and control cohorts. Sixty seven genes were found to have significantly elevated player scores and likely represent significant contributors to the genetic coordination underlying ASD. Using network and cross-study analysis, we found that these genes are involved in biological pathways known to be affected in the autism cases and that a subset directly interact with several genes known to have strong associations to autism. These findings suggest that coalitional game theory can be applied to large-scale genomic data to identify hidden yet influential players in complex polygenic disorders such as autism.

Studies on autism spectrum disorder (ASD) have amassed substantial evidence for the role of genetics in the disease's phenotypic manifestation. A large number of coding and non-coding variants with low penetrance likely act in a combinatorial manner to explain the variable forms of ASD. However, many of these combined interactions, both additive and epistatic, remain undefined. Coalitional game theory (CGT) is an approach that seeks to identify players (individual genetic variants or genes) who tend to improve the performance-association to a disease phenotype of interest-of any coalition (subset of co-occurring genetic variants) they join. This method has been previously applied to boost biologically informative signal from gene expression data and exome sequencing data but remains to be explored in the context of cooperativity among non-coding genomic regions. We describe our extension of previous work, highlighting non-coding chromosomal regions relevant to ASD using CGT on alteration data of 4595 fully sequenced genomes from 756 multiplex families. Genomes were encoded into binary matrices for three types of non-coding regions previously implicated in ASD and separated into ASD (case) and unaffected (control) samples. A player metric, the Shapley value, enabled determination of individual variant contributions in both sets of cohorts. A total of 30 non-coding positions were found to have significantly elevated player scores and likely represent significant contributors to the genetic coordination underlying ASD. Cross-study analyses revealed that a subset of mutated non-coding regions (all of which are in human accelerated regions (HARs)) and related genes are involved in biological pathways or behavioral outcomes known to be affected in autism, suggesting the importance of single nucleotide polymorphisms (SNPs) within HARs in ASD. These findings support the use of CGT in identifying hidden yet influential non-coding players from large-scale genomic data, to better understand the precise underpinnings of complex neurodevelopmental disorders such as autism.

In this paper, we study a routing and travel-mode choice problem for mobility systems with a multimodal transportation network as a "mobility game" with coupled action sets. We formulate an atomic routing game to focus on the travelers' preferences and study the impact on the efficiency of the travelers' behavioral decision-making under rationality and prospect theory. To control the innate inefficiencies, we introduce a mobility "pricing mechanism," in which we model traffic congestion using linear cost functions while also considering the waiting times at different transport hubs. We show that the travelers' selfish actions lead to a pure-strategy Nash equilibrium. We then perform a Price of Anarchy and Price of Stability analysis to establish that the mobility system's inefficiencies remain relatively low and the social welfare at a NE remains close to the social optimum as the number of travelers increases. We deviate from the standard game-theoretic analysis of decision-making by extending our mobility game to capture the subjective behavior of travelers using prospect theory. Finally, we provide a detailed discussion of implementing our proposed mobility game.

Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI.

This study investigates strategies in reasoning about mental states of others, a process that requires theory of mind. It is a first step in studying the cognitive basis of such reasoning, as strategies affect tradeoffs between cognitive resources. Participants were presented with a two-player game that required reasoning about the mental states of the opponent. Game theory literature discerns two candidate strategies that participants could use in this game: either forward reasoning or backward reasoning. Forward reasoning proceeds from the first decision point to the last, whereas backward reasoning proceeds in the opposite direction. Backward reasoning is the only optimal strategy, because the optimal outcome is known at each decision point. Nevertheless, we argue that participants prefer forward reasoning because it is similar to causal reasoning. Causal reasoning, in turn, is prevalent in human reasoning. Eye movements were measured to discern between forward and backward progressions of fixations. The observed fixation sequences corresponded best with forward reasoning. Early in games, the probability of observing a forward progression of fixations is higher than the probability of observing a backward progression. Later in games, the probabilities of forward and backward progressions are similar, which seems to imply that participants were either applying backward reasoning or jumping back to previous decision points while applying forward reasoning. Thus, the game-theoretical favorite strategy, backward reasoning, does seem to exist in human reasoning. However, participants preferred the more familiar, practiced, and prevalent strategy: forward reasoning.

The increasing prevalence of antimicrobial-resistant bacteria drives the need for advanced methods to identify antimicrobial-resistance (AMR) genes in bacterial pathogens. With the availability of whole genome sequences, best-hit methods can be used to identify AMR genes by differentiating unknown sequences with known AMR sequences in existing online repositories. Nevertheless, these methods may not perform well when identifying resistance genes with sequences having low sequence identity with known sequences. We present a machine learning approach that uses protein sequences, with sequence identity ranging between 10% and 90%, as an alternative to conventional DNA sequence alignment-based approaches to identify putative AMR genes in Gram-negative bacteria. By using game theory to choose which protein characteristics to use in our machine learning model, we can predict AMR protein sequences for Gram-negative bacteria with an accuracy ranging from 93% to 99%. In order to obtain similar classification results, identity thresholds as low as 53% were required when using BLASTp.

Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is predicting the spatial arrangement of the various structural elements of RNA. As RNA folding is generally hierarchical, methods involving coarse-grained models hold great promise for this purpose. We present here a novel coarse-grained method for sampling, based on game theory and knowledge-based potentials. This strategy, GARN (Game Algorithm for RNa sampling), is often much faster than previously described techniques and generates large sets of solutions closely resembling the native structure. GARN is thus a suitable starting point for the molecular modeling of large RNAs, particularly those with experimental constraints. GARN is available from: http://garn.lri.fr/.

microRNAs (miRNAs) associate with Ago proteins to post-transcriptionally silence gene expression by targeting mRNAs. To characterize the modes of miRNA-binding, we developed a novel computational framework, called optiCLIP, which considers the reproducibility of the identified peaks among replicates based on the peak overlap. We identified 98 999 binding sites for mouse and human miRNAs, from eleven Ago2 CLIP-seq datasets. Clustering the binding preferences, we found heterogeneity of the mode of binding for different miRNAs. Finally, we set up a quantitative model, named miRgame, based on an adaptation of the game theory. We have developed a new algorithm to translate the miRgame into a score that corresponds to a miRNA degree of occupancy for each Ago2 peak. The degree of occupancy summarizes the number of miRNA-binding sites and miRNAs targeting each binding site, and binding energy of each miRNA::RNA heteroduplex in each peak. Ago peaks were stratified accordingly to the degree of occupancy. Target repression correlates with higher score of degree of occupancy and number of miRNA-binding sites within each Ago peak. We validated the biological performance of our new method on miR-155-5p. In conclusion, our data demonstrate that miRNA-binding sites within each Ago2 CLIP-seq peak synergistically interplay to enhance target repression.

The fungal pathogen Candida albicans colonizes the gastrointestinal (GI) tract of mammalian hosts as a benign commensal. However, in an immunocompromised host, the fungus is capable of causing life-threatening infection. We previously showed that the major transcription factor Efg1p is differentially expressed in GI-colonizing C. albicans cells dependent on the host immune status. To understand the mechanisms that underlie this host-dependent differential gene expression, we utilized mathematical modeling to dissect host-pathogen interactions. Specifically, we used principles of evolutionary game theory to study the mechanism that governs dynamics of EFG1 expression during C. albicans colonization.

High dimensionality of microarray data sets may lead to low efficiency and overfitting. In this paper, a multiphase cooperative game theoretic feature selection approach is proposed for microarray data classification. In the first phase, due to high dimension of microarray data sets, the features are reduced using one of the two filter-based feature selection methods, namely, mutual information and Fisher ratio. In the second phase, Shapley index is used to evaluate the power of each feature. The main innovation of the proposed approach is to employ Qualitative Mutual Information (QMI) for this purpose. The idea of Qualitative Mutual Information causes the selected features to have more stability and this stability helps to deal with the problem of data imbalance and scarcity. In the third phase, a forward selection scheme is applied which uses a scoring function to weight each feature. The performance of the proposed method is compared with other popular feature selection algorithms such as Fisher ratio, minimum redundancy maximum relevance, and previous works on cooperative game based feature selection. The average classification accuracy on eleven microarray data sets shows that the proposed method improves both average accuracy and average stability compared to other approaches.

The transition of energy systems requires policy frameworks and instruments to make both energy suppliers and consumers contribute to the common goal of emission reductions and to fairly allocate costs and benefits among market actors and the government. Assuming that market actors - suppliers and consumers adhering to their economic interests - would benefit from cooperating to mitigate emissions, this study applies a game theory-based approach to investigate the interaction between a local electricity supplier and a group of heating consumers not connected to district heating. Selected policy instruments are tested, and their consequences are analyzed in the context of a representative Nordic municipality. The results show that the auction-based Contract for Difference policy instrument is the most suitable one in the studied Nordic context to achieve significant levels of CO2 emissions reduction. It creates a higher level of strategic interaction between the actors, that would be lacking otherwise, under the form of transfer payments from consumers to supplier, and avoids costs to the general taxpayer. While this is sufficient to promote the investments in renewables by the supplier, additional subsidy policies are required to enable the heating consumers to invest in more capital-intensive energy efficiency measures or biomass heating.

Most unicellular organisms live in communities and express different phenotypes. Many efforts have been made to study the population dynamics of such complex communities of cells, coexisting as well-coordinated units. Minimal models based on ordinary differential equations are powerful tools that can help us understand complex phenomena. They represent an appropriate compromise between complexity and tractability; they allow a profound and comprehensive analysis, which is still easy to understand. Evolutionary game theory is another powerful tool that can help us understand the costs and benefits of the decision a particular cell of a unicellular social organism takes when faced with the challenges of the biotic and abiotic environment. This work is a binocular view at the population dynamics of such a community through the objectives of minimal modelling and evolutionary game theory. We test the behaviour of the community of a unicellular social organism at three levels of antibiotic stress. Even in the absence of the antibiotic, spikes in the fraction of resistant cells can be observed indicating the importance of bet hedging. At moderate level of antibiotic stress, we witness cyclic dynamics reminiscent of the renowned rock-paper-scissors game. At a very high level, the resistant type of strategy is the most favourable.

The sensitivity to fairness undergoes relevant changes across development. Whether such changes depend on primary inequity aversion or on sensitivity to a social norm of fairness is still debated. Using a modified version of the Ultimatum Game that creates informational asymmetries between Proposer and Responder, a previous study showed that both perceptions of fairness and fair behavior depend upon normative expectations, i.e., beliefs about what others expect one should do in a specific situation. Individuals tend to comply with the norm when risking sanctions, but disregard the norm when violations are undetectable. Using the same methodology with children aged 8-10 years, the present study shows that children's beliefs and behaviors differ from what is observed in adults. Playing as Proposers, children show a self-serving bias only when there is a clear informational asymmetry. Playing as Responders, they show a remarkable discrepancy between their normative judgment about fair procedures (a coin toss to determine the offer) and their behavior (rejection of an unfair offer derived from the coin toss), supporting the existence of an outcome bias effect. Finally, our results reveal no influence of theory of mind on children's decision-making behavior.

This study seeks cost-effective strategies for PM2.5 reduction to generate insights into minimizing pollution abatement costs subject to different scenarios. This study theorizes that the cooperation of PM2.5 abatement has potential gains for participants and develop an empirical way to compare the costs and efficiency of PM2.5 abatement involving the variation of environmental conditions. This study revises the cooperative game model in the context of threshold effects using data obtained from the Beijing-Tianjin-Hebei metropolitan cluster in China. In general, the results support the key assertion that cooperation in the metropolitan cluster plays a vital role in optimizing the efficiency and costs of PM2.5 abatement. In addition to extending the application of the revised model, this study provides a way to estimate the costs and the mitigation benefits of meeting the pollution targets for each coparticipant and take the scenario of multiparty cooperation into account as well as the scenarios involving other types of pollutants. The empirical findings have important policy implications for regional shared governance, decentralization, and resource reallocation. Economic incentive-based shared governance and cost reallocation work better than traditional regulations.