93 research outputs found
Learning to trust strangers: an evolutionary perspective
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.
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
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
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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Three Essays on Defending Common-Pool Resources
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Environmental protection often relies on cooperation between individuals in uncoordinated groups. In cases such as the management of common-pool resources, individuals must not only monitor and enforce behavior within their group to prevent over-exploitation. They must also contend with external threats on the resource like poaching. This dissertation studies how individuals cooperate to manage shared resources and deter shared threats.
The first chapter, Deterring poaching of a common-pool resource , considers the problem of deterring a threat that cannot be perfectly observed. I present results from common pool resource experiments designed to examine the ability of a group of resource users, called insiders, to simultaneously manage their own exploitation and defend their resource from encroachment by outsiders. The insiders can use communication, peer monitoring and sanctions to coordinate their decisions. In addition, they can sanction any outsiders they observe. I vary the insiders\u27 ability to observe and sanction the outsiders from no observability to partial and full observability. I find a striking non-monotonicity between observability of the outsiders and levels of poaching. Poaching was higher under partial monitoring than zero monitoring, and was lower and more stable under full monitoring. Although full observability allowed the insiders to better coordinate their own harvests, they were unable to fully deter poaching because their sanctions were far too low and they were unwilling to punish low levels of poaching.
The second chapter, Defending public goods and common-pool resources , studies cooperation and deterrence of a shared threat in different strategic environments. In many real-world social dilemmas, groups of individuals must cooperate to create surplus and defend it from theft. Theft can either foster or discourage collective action. On the one hand, a shared threat can align individual incentives. On the other hand, surplus creation may decrease if individuals are unsure how group members will contribute towards defense. Moreover, there is literature that suggests cooperation is sensitive to whether individual actions confer positive externalities (public goods, PG) or negative externalities (common-pool resources, CPR) on group members -- the cooperation divergence . To examine the relationship between cooperation and defense in different externality settings, I conduct an experiment in which a group of insiders providing a public good or conserving a common-pool resource must coordinate to deter outsiders from stealing the value of their surplus. Our theory predicts that theft will have no different effect on behavior across externality settings. However, I find that it does. Surplus creation is significantly higher in the CPR treatment, while surplus defense is significantly higher in the PG treatment. Across both treatments, I find that the shared threat increases variation within groups, but the effect is more dramatic in the PG treatment.
Finally, the third chaper, Enforcement networks in social dilemmas , studies how enforcement emerges and evolves in the first chapter. Sanctions can increase cooperation in social dilemmas, but they impose a high social cost until a credible threat to non-cooperative behavior is established. Moreover, credible threats depend on enforcement structure. For example, small sanctions implemented by many subjects may have a different impact on behavior than the same volume of sanctions meted out by a single subject. In order to understand how credible threats to deviant behavior emerge, it is therefore necessary to study how enforcement structure emerges and evolves in groups. I study enforcement structure by taking a network approach to data from a social dilemma experiment with peer punishment. The exchange of sanctions between subjects can be framed as a directed, weighted network that evolves, enabling us to use tools from network structure to summarize, predict and simulate behavior. I first visualize and summarize the structure of these networks and show that enforcement structure is non-random and tends to cluster around a few individuals. I then model network formation and network efficiency using an empirical framework that separately considers edge formation (a binary sanctioning event) from edge weight (sanction size) and find that subjects respond more to the act of being sanctioned rather than the volume of sanctions. Finally, I recover the underlying Markov process governing enforcement structure and simulate expected long-run behavior. I conclude with a discussion of how my approach can be used to study generalized exchange networks
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