93 research outputs found

    Learning to trust strangers: an evolutionary perspective

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    What if living in a relatively trustworthy society was sufficient to blindly trust strangers? In this paper we interpret generalized trust as a learning process and analyse the trust game paradox in light of the replicator dynamics. Given that trust inevitably implies doubts about others, we assume incomplete information and study the dynamics of trust in buyer-supplier purchase transactions. Considering a world made of “good” and “bad” suppliers, we show that the trust game admits a unique evolutionarily stable strategy: buyers may trust strangers if, on the whole, it is not too risky to do so. Examining the situation where some players may play, either as trustor or as trustee, we show that this result is robust.

    Evaluation of human movement qualities: A methodology based on transferable-utility games on graphs.

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    Abstract A novel computational method for the analysis of expressive full-body movement qualities is introduced, which exploits concepts and tools from graph theory and game theory. The human skeletal structure is modeled as an undirected graph, where the joints are the vertices and the edge set contains both physical and nonphysical links. Physical links correspond to connections between adjacent physical body joints (e.g., the forearm, which connects the elbow to the wrist). Nonphysical links act as \u201cbridges\u201d between parts of the body not directly connected by the skeletal structure, but sharing very similar feature values. The edge weights depend on features obtained by using Motion Capture data. Then, a mathematical game is constructed over the graph structure, where the vertices represent the players and the edges represent communication channels between them. Hence, the body movement is modeled in terms of a game built on the graph structure. Since the vertices and the edges contribute to the overall quality of the movement, the adopted game-theoretical model is of cooperative nature. A game-theoretical concept, called Shapley value, is exploited as a centrality index to estimate the contribution of each vertex to a shared goal (e.g., to the way a particular movement quality is transferred among the vertices). The proposed method is applied to a data set of Motion Capture data of subjects performing expressive movements, recorded in the framework of the H2020-ICT-2015 EU Project WhoLoDance, Project no. 688865. Results are presented: development of novel method, contribution to the scientific community with a new data corpus, application the discussed method to 100 movement recordings and creation of database archive of stimuli for further use in research studies in the framework of the WhoLoDance Project

    Essays on modeling and analysis of dynamic sociotechnical systems

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    A sociotechnical system is a collection of humans and algorithms that interact under the partial supervision of a decentralized controller. These systems often display in- tricate dynamics and can be characterized by their unique emergent behavior. In this work, we describe, analyze, and model aspects of three distinct classes of sociotech- nical systems: financial markets, social media platforms, and elections. Though our work is diverse in subject matter content, it is unified though the study of evolution- and adaptation-driven change in social systems and the development of methods used to infer this change. We first analyze evolutionary financial market microstructure dynamics in the context of an agent-based model (ABM). The ABM’s matching engine implements a frequent batch auction, a recently-developed type of price-discovery mechanism. We subject simple agents to evolutionary pressure using a variety of selection mech- anisms, demonstrating that quantile-based selection mechanisms are associated with lower market-wide volatility. We then evolve deep neural networks in the ABM and demonstrate that elite individuals are profitable in backtesting on real foreign ex- change data, even though their fitness had never been evaluated on any real financial data during evolution. We then turn to the extraction of multi-timescale functional signals from large panels of timeseries generated by sociotechnical systems. We introduce the discrete shocklet transform (DST) and associated similarity search algorithm, the shocklet transform and ranking (STAR) algorithm, to accomplish this task. We empirically demonstrate the STAR algorithm’s invariance to quantitative functional parameteri- zation and provide use case examples. The STAR algorithm compares favorably with Twitter’s anomaly detection algorithm on a feature extraction task. We close by using STAR to automatically construct a narrative timeline of societally-significant events using a panel of Twitter word usage timeseries. Finally, we model strategic interactions between the foreign intelligence service (Red team) of a country that is attempting to interfere with an election occurring in another country, and the domestic intelligence service of the country in which the election is taking place (Blue team). We derive subgame-perfect Nash equilibrium strategies for both Red and Blue and demonstrate the emergence of arms race inter- ference dynamics when either player has “all-or-nothing” attitudes about the result of the interference episode. We then confront our model with data from the 2016 U.S. presidential election contest, in which Russian military intelligence interfered. We demonstrate that our model captures the qualitative dynamics of this interference for most of the time under stud

    Recommended Priorities for Research on Ecological Impacts of Ocean and Coastal Acidification in the U.S. Mid-Atlantic

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    The estuaries and continental shelf system of the United States Mid-Atlantic are subject to ocean acidification driven by atmospheric CO2, and coastal acidification caused by nearshore and land-sea interactions that include biological, chemical, and physical processes. These processes include freshwater and nutrient input from rivers and groundwater; tidally-driven outwelling of nutrients, inorganic carbon, alkalinity; high productivity and respiration; and hypoxia. Hence, these complex dynamic systems exhibit substantial daily, seasonal, and interannual variability that is not well captured by current acidification research on Mid-Atlantic organisms and ecosystems. We present recommendations for research priorities that target better understanding of the ecological impacts of acidification in the U. S. Mid-Atlantic region. Suggested priorities are: 1) Determining the impact of multiple stressors on our resource species as well as the magnitude of acidification; 2) Filling information gaps on major taxa and regionally important species in different life stages to improve understanding of their response to variable temporal scales and sources of acidification; 3) Improving experimental approaches to incorporate realistic environmental variability and gradients, include interactions with other environmental stressors, increase transferability to other systems or organisms, and evaluate community and ecosystem response; 4) Determining the capacity of important species to acclimate or adapt to changing ocean conditions; 5) Considering multi-disciplinary, ecosystem-level research that examines acidification impacts on biodiversity and biotic interactions; and 6) Connecting potential acidification-induced ecological impacts to ecosystem services and the economy. These recommendations, while developed for the Mid-Atlantic, can be applicable to other regions will help align research towards knowledge of potential larger-scale ecological and economic impacts
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