Статті в журналах: "Computationnal social choice"

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Procaccia, Ariel D. "Computational social choice." XRDS: Crossroads, The ACM Magazine for Students 18, no. 2 (December 2011): 31–34. http://dx.doi.org/10.1145/2043236.2043249.

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Kelly, Jerry S. "Social choice and computational complexity." Journal of Mathematical Economics 17, no. 1 (January 1988): 1–8. http://dx.doi.org/10.1016/0304-4068(88)90022-5.

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Endriss, Ulle. "Computational Social Choice: Prospects and Challenges." Procedia Computer Science 7 (2011): 68–72. http://dx.doi.org/10.1016/j.procs.2011.12.022.

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Mandali, Alekhya, Claire Gillan, and Valerie Voon. "27 The coexistence of social withdrawal and impulsivity: a trans-diagnostic approach." Journal of Neurology, Neurosurgery & Psychiatry 91, no. 8 (July 20, 2020): e19.1-e19. http://dx.doi.org/10.1136/jnnp-2020-bnpa.44.

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IntroductionSocial anxiety disorder or phobia (SAD) is a debilitating condition, where an individual experiences overwhelming fear to situations involving social interactions. Prototypically, SAD presents as shy, submissive, inhibited, and risk- aversive behaviours. Contrastingly, an atypical sub-group show impulsive, aggressive, novelty-seeking behaviours along with severe substance abuse problems. In scenarios, where there is co-existence of polar opposite symptoms, trans-diagnostic approaches extrapolate the characteristics of a disorder as a continuum rather than a categorical one. Data-driven computational models such as drift diffusion model utilize behavioural measures and extract potential markers that reflect the activity of specific brain networks. Here, we aim to analyse and correlate the psychological traits with computational estimates of behaviour during risk-taking and value based decision making.MethodsWe used the data from 1400 participants who completed the 2 stage sequential learning task. We focused on the second stage of the task, where the reward probabilities of the choices are stochastic. The computational measures were estimated for two scenarios i.e. when the participants made 1) accurate choices and 2) risky choices (the choice with maximum variance in reward probability was labelled as risky). This computation was performed for all the trials across all the participants. We then used choice–(risky vs non-risky or correct vs incorrect) and response time as inputs to the hierarchical drift diffusion model to extract threshold (a), drift rate (v) and response bias (z) parameters. The computational parameters were then correlated with the 3 psychological factors that span the compulsive, anxiety- depression and the social withdrawal spectrum.ResultsThe computational parameters from both accuracy and risk taking scenarios of the sequential learning task were correlated with the 3 factors. While controlling for IQ and age, we found a generalized correlation which is significant between the threshold parameter(‘a’) and social withdrawal, with the former estimate being negatively correlated (Accuracy: |r| = -0.078, p=0.003; Risk: |r| = -0.075, p=0.005) with the latter. This relation was not observed with regard to anxiety-depression and compulsive traits.ConclusionsWe show that individuals with higher social withdrawal levels are impulsive as they accumulate less evidence while making a choice. This behaviour holds irrespective of the choice being chosen is an optimal or a risky one. Critically, we show how a trans-diagnostic approach of integrating computational model and psychological questionnaires can reveal the existence of psychological traits as a continuum.

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ENDRISS, ULLE. "The 1st international workshop on computational social choice." Knowledge Engineering Review 23, no. 2 (June 2008): 213–15. http://dx.doi.org/10.1017/s0269888908001343.

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AbsractComputational social choice is a new discipline currently emerging at the interface of social choice theory and computer science. It is concerned with the application of computational techniques to the study of social choice mechanisms, and with the integration of social choice paradigms into computing. The first international workshop specifically dedicated to this topic took place in December 2006 in Amsterdam, attracting a mix of computer scientists, people working in artificial intelligence and multiagent systems, economists, game and social choice theorists, logicians, mathematicians, philosophers, and psychologists as participants.

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Brandt, Felix, and William S. Zwicker. "Special Issue on Computational Foundations of Social Choice." Mathematical Social Sciences 64, no. 1 (July 2012): 1. http://dx.doi.org/10.1016/j.mathsocsci.2012.05.003.

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Ismaili, Anisse, and Patrice Perny. "Computational social choice for coordination in agent networks." Annals of Mathematics and Artificial Intelligence 77, no. 3-4 (June 13, 2015): 335–59. http://dx.doi.org/10.1007/s10472-015-9462-x.

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Elkind, Edith, and Jérôme Lang. "Guest editorial: special issue on computational social choice." Autonomous Agents and Multi-Agent Systems 22, no. 1 (October 15, 2010): 1–3. http://dx.doi.org/10.1007/s10458-010-9155-0.

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Kim, Jaejoong, and Bumseok Jeong. "Expecting social punishment facilitates control over a decision under uncertainty by recruiting medial prefrontal cortex." Social Cognitive and Affective Neuroscience 15, no. 11 (November 1, 2020): 1260–70. http://dx.doi.org/10.1093/scan/nsaa145.

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Abstract In many decision-making situations, sub-optimal choices are increased by uncertainty. However, when wrong choices could lead to social punishment, such as blame, people might try to improve their performance by minimizing sub-optimal choices, which could be achieved by increasing the subjective cost of errors, thereby globally reducing decision noise or reducing an uncertainty-induced component of decision noise. In this functional magnetic resonance imaging (fMRI) study, 46 participants performed a choice task in which the probability of a correct choice with a given cue and the conditional probability of blame feedback (by making an incorrect choice) changed continuously. By comparing computational models of behaviour, we found that participants optimized their performance by preferentially reducing a component of decision noise associated with uncertainty. Simultaneously, expecting blame significantly deteriorated participants’ mood. Model-based fMRI analyses and dynamic causal modelling indicate that the optimization mechanism based on the expectation of being blamed would be controlled by a neural circuit centred on the right medial prefrontal cortex. These results show novel behavioural and neural mechanisms regarding how humans optimize uncertain decisions under the expectation of being blamed.

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Bredereck, Robert, Jiehua Chen, Piotr Faliszewski, Jiong Guo, Rolf Niedermeier, and Gerhard J. Woeginger. "Parameterized algorithmics for computational social choice: Nine research challenges." Tsinghua Science and Technology 19, no. 4 (August 2014): 358–73. http://dx.doi.org/10.1109/tst.2014.6867518.

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Tideman, T. Nicolaus, and Florenz Plassmann. "Developing the aggregate empirical side of computational social choice." Annals of Mathematics and Artificial Intelligence 68, no. 1-3 (May 31, 2013): 31–64. http://dx.doi.org/10.1007/s10472-013-9360-z.

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Lev, Omer. "Towards a More Burkean Approach to Computational Social Choice." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (March 24, 2024): 22607–13. http://dx.doi.org/10.1609/aaai.v38i20.30270.

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In the last few years, a lot of the activity of the computational social choice community has focused on novel mechanisms for reaching decisions by large groups of people. While this research makes meaningful scientific contributions, many of these mechanisms are not quite useful in realistic decision-making settings. Moreover, their radicalism ignores the centuries-old experience we have with large-scale human decision-making, and what it teaches us about what works. We believe it is important the community engage with mechanisms which are widely-used in the real world, as they may hold a key to a deeper understanding of how people reach decisions and the way that helps them do that productively. Moreover, letting the community bring its analysis and understanding to these will allow for algorithmic suggestions that have some chance of being implemented (and, thus, can contribute to the public debate on these topics). In particular, we highlight the relatively less-investigated role of parties and grouping of voters and candidates, and the role of executive capacity in analyzing decision-making structures.

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Kadak, Kevin, and Noam Miller. "Follow the straggler: zebrafish use a simple heuristic for collective decision-making." Proceedings of the Royal Society B: Biological Sciences 287, no. 1940 (December 2, 2020): 20202690. http://dx.doi.org/10.1098/rspb.2020.2690.

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Animal groups often make decisions sequentially, from the front to the back of the group. In such cases, individuals can use the choices made by earlier ranks, a form of social information, to inform their own choice. The optimal strategy for such decisions has been explored in models which differ on, for example, whether or not agents take into account the sequence of observed choices. The models demonstrate that choices made later in a sequence are more informative, but it is not clear if animals use this information or rely instead on simpler heuristics, such as quorum rules. We show that a simple rule ‘copy the last observed choice', gives similar predictions to those of optimal models for most likely sequences. We trained groups of zebrafish to choose one arm of a Y-maze and used them to demonstrate various sequences to naive fish. We show that the naive fish appear to use a simple rule, most often copying the choice of the last demonstrator, which results in near-optimal choices at a fraction of the computational cost.

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Chevaleyre, Yann, Ulle Endriss, Jérôme Lang, and Nicolas Maudet. "Preference Handling in Combinatorial Domains: From AI to Social Choice." AI Magazine 29, no. 4 (December 28, 2008): 37. http://dx.doi.org/10.1609/aimag.v29i4.2201.

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In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multi-attribute) structure. We give an introduction to preference handling in combinatorial domains in the context of collective decision making, and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains, and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, known as computational social choice, that brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.

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Nipkow, Tobias. "Social Choice Theory in HOL." Journal of Automated Reasoning 43, no. 3 (August 1, 2009): 289–304. http://dx.doi.org/10.1007/s10817-009-9147-4.

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Dodevska, Zorica. "Computational Social Choice and challenges оf voting in multi-agent systems". Tehnika 74, № 5 (2019): 724–30. http://dx.doi.org/10.5937/tehnika1905724d.

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Nagaraj, S. V. "Review of Trends in Computational Social Choice Edited by Ulle Endriss." ACM SIGACT News 49, no. 2 (June 13, 2018): 14–17. http://dx.doi.org/10.1145/3232679.3232684.

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Mikhailov, I. F. "Computational approach to social knowledge." Philosophy of Science and Technology 26, no. 2 (2021): 23–37. http://dx.doi.org/10.21146/2413-9084-2021-26-1-23-37.

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Social and cognitive sciences have always faced the choice: either to meet the methodologi- cal standards given by successful natural sciences or to rely on their own. Talking about the conversion of knowledge into technology, the second way did not bring great success. The first way implies two alternative opportunities: reductionism or discovery of proprietary general laws. None of these chances have been realized with any satisfactory results, too. Methodological analysis shows that, to achieve significant progress in social sciences, what is missed there is not new facts or definitions but new conceptual schemes. The reason, as the author supposes, is the nomothetic approach being applied to systems with high degree of complexity and hierarchy. If we assume that social structures and processes are built upon cognitive psychological structures and processes, the former inherit the distributed computational architecture of the latter. The paper analyzes various conceptions of computations in order to determine their relevance to the task of building computational social science. The author offers a “generic” definition of computations as a process carried out by a computational system if the latter is understood as a mechanism of some representation. According to the author, the computationalization of social science implies “naturalization” of computations. This requires a theory that would explain the mechanism of growing complexity and hierarchy of natural (in particular, social) computational systems. As a method for constructing such a science, a kind of reverse engineering is proposed, which is recreation of a computational algorithmic scheme of social tissue by the determination and recombination of “social primitives” – elementary operations of social interaction.

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Chatterjee, Siddharth, and Arunava Sen. "Automated Reasoning in Social Choice Theory: Some Remarks." Mathematics in Computer Science 8, no. 1 (March 2014): 5–10. http://dx.doi.org/10.1007/s11786-014-0177-x.

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Cohen, Robin, and John A. Doucette. "A testbed to enable comparisons between competing approaches for computational social choice." Big Data and Information Analytics 1, no. 4 (April 2017): 309–40. http://dx.doi.org/10.3934/bdia.2016013.

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Goldsmith, Judy, and Jörg Rothe. "Algorithms, approximation, and empirical studies in behavioral and computational social choice—Preface." Annals of Mathematics and Artificial Intelligence 68, no. 1-3 (July 2013): 3–4. http://dx.doi.org/10.1007/s10472-013-9380-8.

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Brandt, Felix, and Christian Geist. "Finding Strategyproof Social Choice Functions via SAT Solving." Journal of Artificial Intelligence Research 55 (March 4, 2016): 565–602. http://dx.doi.org/10.1613/jair.4959.

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A promising direction in computational social choice is to address research problems using computer-aided proving techniques. In particular with SAT solvers, this approach has been shown to be viable not only for proving classic impossibility theorems such as Arrow's Theorem but also for finding new impossibilities in the context of preference extensions. In this paper, we demonstrate that these computer-aided techniques can also be applied to improve our understanding of strategyproof irresolute social choice functions. These functions, however, requires a more evolved encoding as otherwise the search space rapidly becomes much too large. Our contribution is two-fold: We present an efficient encoding for translating such problems to SAT and leverage this encoding to prove new results about strategyproofness with respect to Kelly's and Fishburn's preference extensions. For example, we show that no Pareto-optimal majoritarian social choice function satisfies Fishburn-strategyproofness. Furthermore, we explain how human-readable proofs of such results can be extracted from minimal unsatisfiable cores of the corresponding SAT formulas.

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Demuynck, Thomas. "The computational complexity of rationalizing Pareto optimal choice behavior." Social Choice and Welfare 42, no. 3 (March 25, 2013): 529–49. http://dx.doi.org/10.1007/s00355-013-0735-1.

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Xia, Lirong, and Weiqiang Zheng. "The Smoothed Complexity of Computing Kemeny and Slater Rankings." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 5742–50. http://dx.doi.org/10.1609/aaai.v35i6.16720.

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The computational complexity of winner determination under common voting rules is a classical and fundamental topic in the field of computational social choice. Previous work has established the NP-hardness of winner determination under some commonly-studied voting rules, such as the Kemeny rule and the Slater rule. In a recent position paper, Baumeister, Hogrebe, and Rothe (2020) questioned the relevance of the worst-case nature of NP-hardness in social choice and proposed to conduct smoothed complexity analysis (Spielman and Teng 2009) under Blaser and Manthey’s (2015) framework. In this paper, we develop the first smoothed complexity results for winner determination in voting. We prove the smoothed hardness of Kemeny and Slater using the classical smoothed runtime analysis, and prove a parameterized typical-case smoothed easiness result for Kemeny. We also make an attempt of applying Blaser and Manthey’s (2015) smoothed complexity framework in social choice contexts by proving that the framework categorizes an always-exponential-time brute force search algorithm as being smoothed poly-time, under a natural noise model based on the well-studied Mallows model in social choice and statistics. Overall, our results show that smoothed complexity analysis in computational social choice is a challenging and fruitful topic.

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Istratov, Victor A. "Computational concept for human food choice and eating behaviour." Journal Of Applied Informatics 18, no. 3 (June 16, 2023): 115–31. http://dx.doi.org/10.37791/2687-0649-2023-18-3-115-131.

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An inadequate diet can cause a number of illnesses with some of them posing major threats for humanity. Poor diet largely originates from behavioral and social issues rather than environmental factors. With simulation being a grand tool to analyze and address behavior issues, relatively few studies focus on computational modeling of nutrition at behavioural level. Furthermore, we have overviewed several popular approaches to computational modeling and simulating dietary decision-making and found no clear favorite. Further still, modelers rarely pay attention to one of the key behavioural factors – motivation. In the vast majority of models, either motivation is assumed to be exogenously given and, hence, is left out of the model, or motivation is not taken into account in any form, even though ignoring incentives significantly reduces adaptive capabilities of any human-like goal-directed model entity. We aimed to outline a modelling approach that would fit into the food choice topic and would improve on the available models. This implies offering an intelligible algorithm that would be easily applied to statistical data yet offering a depth of analysis despite its seeming simplicity. Thus, we present our view of the food choice simulation problem which employs eating incentives and an original choice mechanism that is different both from traditional maximizing approaches common to economics and artificial intelligence research and from the dominant psychological computational approaches. We outlined the programming conceptual algorithm that involves sequential incentive (which can result from the biological necessities, social, intellectual or spiritual needs alike) selection, incentive-foodstuff coupling (a relation can be either fixed or dynamic) and elimination of undesirable food options based on incentives ranking (qualitative ranking seems to be preferable over quantitative ranking, forasmuch as it resembles the way of thinking of a regular person more closely) supplemented by pseudocode segments. The algorithm suits agent-based simulation paradigm, yet it is not tied to it and can be fitted with other simulation approaches as well. The algorithm is supposed to be implemented in Java. Since the offered algorithm is conceptual it requires an implementation to bring about robust conclusions which is our goal to reach next.

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Serrano, Roberto. "The Theory of Implementation of Social Choice Rules." SIAM Review 46, no. 3 (January 2004): 377–414. http://dx.doi.org/10.1137/s0036144503435945.

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Karanikolas, Nikos, Pierre Bisquert, Patrice Buche, Christos Kaklamanis, and Rallou Thomopoulos. "A Decision Support Tool for Agricultural Applications Based on Computational Social Choice and Argumentation." International Journal of Agricultural and Environmental Information Systems 9, no. 3 (July 2018): 54–73. http://dx.doi.org/10.4018/ijaeis.2018070104.

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In the current article, the authors describe an applied procedure to support collective decision making for applications in agriculture. An extended 2-page abstract of this paper has been accepted by the EFITA WCCA congress and this manuscript is an extended version of this submission. The problem the authors are facing in this paper is how to reach the best decision regarding issues coming from agricultural engineering with the aid of Computational Social Choice (CSC) and Argumentation Framework (AF). In the literature of decision-making, several approaches from the domains of CSC and AF have been used autonomously to support decisions. It is our belief that with the combination of these two fields the authors can propose socially fair decisions which take into account both (1) the involved agents' preferences and (2) the justifications behind these preferences. Therefore, this article implements a software tool for decision-making which is composed of two main systems, i.e., the social choice system and the deliberation system. In this article, the authors describe thoroughly the social choice system of our tool and how it can be applied to different alternatives on the valorization of materials coming from agriculture. As an example, that is demonstrated an application of our tool in the context of Ecobiocap European project where several decision problems are to be addressed. These decision problems consist in finding the best solutions for questions regarding food packaging and end-of-life management.

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Fehr, Ernst, and Antonio Rangel. "Neuroeconomic Foundations of Economic Choice—Recent Advances." Journal of Economic Perspectives 25, no. 4 (November 1, 2011): 3–30. http://dx.doi.org/10.1257/jep.25.4.3.

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Neuroeconomics combines methods and theories from neuroscience psychology, economics, and computer science in an effort to produce detailed computational and neurobiological accounts of the decision-making process that can serve as a common foundation for understanding human behavior across the natural and social sciences. Because neuroeconomics is a young discipline, a sufficiently sound structural model of how the brain makes choices is not yet available. However, the contours of such a computational model are beginning to arise; and, given the rapid progress, there is reason to be hopeful that the field will eventually put together a satisfactory structural model. This paper has two goals: First, we provide an overview of what has been learned about how the brain makes choices in two types of situations: simple choices among small numbers of familiar stimuli (like choosing between an apple or an orange), and more complex choices involving tradeoffs between immediate and future consequences (like eating a healthy apple or a less-healthy chocolate cake). Second, we show that, even at this early stage, insights with important implications for economics have already been gained.

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Farmer, Harry, Uri Hertz, and Antonia F. de C. Hamilton. "The neural basis of shared preference learning." Social Cognitive and Affective Neuroscience 14, no. 10 (October 1, 2019): 1061–72. http://dx.doi.org/10.1093/scan/nsz076.

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Abstract During our daily lives, we often learn about the similarity of the traits and preferences of others to our own and use that information during our social interactions. However, it is unclear how the brain represents similarity between the self and others. One possible mechanism is to track similarity to oneself regardless of the identity of the other (Similarity account); an alternative is to track each other person in terms of consistency of their choice similarity with respect to the choices they have made before (consistency account). Our study combined functional Magnetic Resonance Imaging (fMRI) and computational modelling of reinforcement learning (RL) to investigate the neural processes that underlie learning about preference similarity. Participants chose which of two pieces of artwork they preferred and saw the choices of one agent who usually shared their preference and another agent who usually did not. We modelled neural activation with RL models based on the similarity and consistency accounts. Our results showed that activity in brain areas linked to reward and social cognition followed the consistency account. Our findings suggest that impressions of other people can be calculated in a person-specific manner, which assumes that each individual behaves consistently with their past choices.

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Canals, Catalina, Eric Goles, Aldo Mascareño, Sergio Rica, and Gonzalo A. Ruz. "School Choice in a Market Environment: Individual versus Social Expectations." Complexity 2018 (December 2, 2018): 1–11. http://dx.doi.org/10.1155/2018/3793095.

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School choice is a key factor connecting personal preferences (beliefs, desires, and needs) and school offer in education markets. While it is assumed that preferences are highly individualistic forms of expectations by means of which parents select schools satisfying their internal moral standards, this paper argues that a better matching between parental preferences and school offer is achieved when individuals take into account their relevant network vicinity, thereby constructing social expectations regarding school choice. We develop two related models (individual expectations and social expectations) and prove that they are driven by a Lyapunov function, obtaining that both models converge to fixed points. Also, we assess their performance by conducting computational simulations. While the individual expectations model shows a probabilistic transition and a critical threshold below which preferences concentrate in a few schools and a significant amount of students is left unattended by the school offer, the social expectations model presents a smooth dynamics in which most of the schools have students all the time and no students are left out. We discuss our results considering key topics of the empirical research on school choice in educational market environments and conclude that social expectations contribute to improve information and lead to a better matching between school offer and parental preferences.

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Manzo, Gianluca. "Is rational choice theory still a rational choice of theory? A response to Opp." Social Science Information 52, no. 3 (August 5, 2013): 361–82. http://dx.doi.org/10.1177/0539018413488477.

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Authoritative rational choice theorists continue to argue that wide variants of rational choice theory should be regarded as the best starting-point to formulate theoretical hypotheses on the micro foundations of complex macro-level social dynamics. Building on recent writings on neo-classical rational choice theory, on behavioral economics and on cognitive psychology, the present article challenges this view and argues that: (1) neo-classical rational choice theory is an astonishingly malleable and powerful analytical device whose descriptive accuracy is nevertheless limited to a very specific class of choice settings; (2) the ‘wide’ sociological rational choice theory does not add anything original to the neo-classical framework on a conceptual level and it is also methodologically weaker; (3) at least four alternative action-oriented approaches that reject portrayal of actors as computational devices operating over probability distributions can be used to design sociological explanations that are descriptively accurate at the micro level.

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Tanaka, Yasuhito. "A topological proof of Eliaz’s unified theorem of social choice theory." Applied Mathematics and Computation 176, no. 1 (May 2006): 83–90. http://dx.doi.org/10.1016/j.amc.2005.09.055.

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Rossi, Francesca, and K. Brent Venable. "Guest editorial: revised selected papers from the AMAI 2014 special track on Computational Social Choice." Annals of Mathematics and Artificial Intelligence 77, no. 3-4 (July 26, 2016): 157–58. http://dx.doi.org/10.1007/s10472-016-9518-6.

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Conroy-Beam, Daniel. "Euclidean Mate Value and Power of Choice on the Mating Market." Personality and Social Psychology Bulletin 44, no. 2 (October 28, 2017): 252–64. http://dx.doi.org/10.1177/0146167217739262.

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Three studies tested the hypothesis that human mate choice psychology uses a Euclidean algorithm to integrate mate preferences into estimates of mate value. In Study 1, a series of agent-based models identify a pattern of results relatively unique to mating markets where individuals high in Euclidean mate value experience greater power of choice: strong preference fulfillment overall and correlations between mate value and (a) preference fulfillment, (b) ideal standards, and (c) partner mate value. Studies 2 and 3 demonstrated that this pattern of results that emerges in human romantic relationships, is specific to mate value as a long-term partner, and is not accounted for by participant biases. These results suggest that human mate choice psychology uses a Euclidean algorithm to integrate mate preferences in mate choice, providing insight into the computational design of human mating psychology and validating this algorithm as a useful tool for future research.

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Neath, Andrew A., Joseph E. Cavanaugh, and Adam G. Weyhaupt. "Model evaluation, discrepancy function estimation, and social choice theory." Computational Statistics 30, no. 1 (September 27, 2014): 231–49. http://dx.doi.org/10.1007/s00180-014-0532-z.

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Argamon, Shlomo Engelson. "Register in computational language research." Register Studies 1, no. 1 (April 26, 2019): 100–135. http://dx.doi.org/10.1075/rs.18015.arg.

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Abstract Shlomo Argamon is Professor of Computer Science and Director of the Master of Data Science Program at the Illinois Institute of Technology (USA). In this article, he reflects on the current and potential relationship between register and the field of computational linguistics. He applies his expertise in computational linguistics and machine learning to a variety of problems in natural language processing. These include stylistic variation, forensic linguistics, authorship attribution, and biomedical informatics. He is particularly interested in the linguistic structures used by speakers and writers, including linguistic choices that are influenced by social variables such as age, gender, and register, as well as linguistic choices that are unique or distinctive to the style of individual authors. Argamon has been a pioneer in computational linguistics and NLP research in his efforts to account for and explore register variation. His computational linguistic research on register draws inspiration from Systemic Functional Linguistics, Biber’s multi-dimensional approach to register variation, as well as his own extensive experience accounting for variation within and across text types and authors. Argamon has applied computational methods to text classification and description across registers – including blogs, academic disciplines, and news writing – as well as the interaction between register and other social variables, such as age and gender. His cutting-edge research in these areas is certain to have a lasting impact on the future of computational linguistics and NLP.

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37

Baek, Seung Hyun, Alberto Garcia-Diaz, and Yuanshun Dai. "Multi-choice wavelet thresholding based binary classification method." Methodology 16, no. 2 (June 18, 2020): 127–46. http://dx.doi.org/10.5964/meth.2787.

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Data mining is one of the most effective statistical methodologies to investigate a variety of problems in areas including pattern recognition, machine learning, bioinformatics, chemometrics, and statistics. In particular, statistically-sophisticated procedures that emphasize on reliability of results and computational efficiency are required for the analysis of high-dimensional data. Optimization principles can play a significant role in the rationalization and validation of specialized data mining procedures. This paper presents a novel methodology which is Multi-Choice Wavelet Thresholding (MCWT) based three-step methodology consists of three processes: perception (dimension reduction), decision (feature ranking), and cognition (model selection). In these steps three concepts known as wavelet thresholding, support vector machines for classification and information complexity are integrated to evaluate learning models. Three published data sets are used to illustrate the proposed methodology. Additionally, performance comparisons with recent and widely applied methods are shown.

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38

Marchal, F., and K. Nagel. "Modeling Location Choice of Secondary Activities with a Social Network of Cooperative Agents." Transportation Research Record: Journal of the Transportation Research Board 1935, no. 1 (January 2005): 141–46. http://dx.doi.org/10.1177/0361198105193500116.

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Activity-based models in transportation science focus on the description of human trips and activities. Modeling the spatial decision for so-called secondary activities is addressed in this paper. Given both home and work locations, where do individuals perform activities such as shopping and leisure? Simulation of these decisions using random utility models requires a full enumeration of possible outcomes. For large data sets, it becomes computationally unfeasible because of the combinatorial complexity. To overcome that limitation, a model is proposed in which agents have limited, accurate information about a small subset of the overall spatial environment. Agents are interconnected by a social network through which they can exchange information. This approach has several advantages compared with the explicit simulation of a standard random utility model: ( a) it computes plausible choice sets in reasonable computing times, ( b) it can be extended easily to integrate further empirical evidence about travel behavior, and ( c) it provides a useful framework to study the propagation of any newly available information. This paper emphasizes the computational efficiency of the approach for real-world examples.

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39

Salles, Maurice. "Felix Brandt, Vincent Conitzer, Ulle Endriss, Jerôme Lang, and Ariel Procaccia (eds), Handbook of Computational Social Choice." OEconomia, no. 7-4 (December 1, 2017): 609–18. http://dx.doi.org/10.4000/oeconomia.2818.

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40

Teixeira, Miguel, Pedro M. d’Orey, and Zafeiris Kokkinogenis. "Simulating collective decision-making for autonomous vehicles coordination enabled by vehicular networks: A computational social choice perspective." Simulation Modelling Practice and Theory 98 (January 2020): 101983. http://dx.doi.org/10.1016/j.simpat.2019.101983.

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41

Garcia-Lapresta, Jose Luis, Bonifacio Llamazares, and Miguel Martinez-Panero. "A Social Choice Analysis of the Borda Rule in a General Linguistic Framework." International Journal of Computational Intelligence Systems 3, no. 4 (2010): 501. http://dx.doi.org/10.2991/ijcis.2010.3.4.9.

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42

García-Lapresta, José Luis, Bonifacio Llamazares, and Miguel Martínez-Panero. "A Social Choice Analysis of the Borda Rule in a General Linguistic Framework." International Journal of Computational Intelligence Systems 3, no. 4 (October 2010): 501–13. http://dx.doi.org/10.1080/18756891.2010.9727717.

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43

García-Lapresta, José Luis, Bonifacio Llamazares, and Miguel Martínez-Panero. "A Social Choice Analysis of the Borda Rule in a General Linguistic Framework." International Journal of Computational Intelligence Systems 3, no. 4 (October 2010): 501–13. http://dx.doi.org/10.2991/ijcis.2010.3.4.10.

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AbstractIn this paper the Borda rule is extended by allowing the voters to show their preferences among alternatives through linguistic labels. To this aim, we need to add them up for assigning a qualification to each alternative and then to compare such qualifications. Theoretically, all these assessments and comparisons fall into a totally ordered commutative monoid generated by the initial set of linguistic labels. Practically, we show an example which illustrates the suitability of this linguistic approach. Finally, some interesting properties for this Borda rule are proven in the Social Choice context.

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44

Başar, Tamer. "Social Choice and Multicriterion Decision-Making (Kenneth J. Arrow and Herve Raynaud)." SIAM Review 30, no. 1 (March 1988): 137–38. http://dx.doi.org/10.1137/1030018.

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45

Valdivia López, Marcos. "CHOICE AND NON-COORDINATION BEHAVIOR IN A GLOBAL AND LOCAL INFORMATION SETTING: A COMPUTATIONAL APPROACH." Revista Pueblos y fronteras digital 5, no. 9 (June 1, 2010): 4. http://dx.doi.org/10.22201/cimsur.18704115e.2010.9.158.

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This paper studies conformity effects on individual choice when both local and global information are present. A standard discrete choice model that incorporates the social interaction effect to assign choice probabilities to agents is studied. The model is analyzed by using computational simulations. Agents are dispersed in a two dimensional toroidal lattice and they can gather information either from their von Neumann neighbors or from the whole community of agents. Agent heterogeneity is introduced through diversity in private incentives among agents. The main results of the simulations show that the effects of global and local information can produce conflicting informational data streams in agents, making it likely that unstable and volatile aggregate choice emerges. Likewise, the results indicate that the interaction between global and local information affects the shape of the distribution of the size of informational cascades. RESUMEN Esta investigación analiza los efectos de conformidad en las decisiones de los individuos en un contexto en donde los agentes económicos acceden a información global y local. Un modelo de decisión discreta que incorpora el efecto de interacción social es analizado por medio de simulaciones computacionales. Los agentes bajo estudio están dispersos en una cuadrícula de dos dimensiones en donde recolectan información ya sea a partir de sus vecinos von Neumann o de la comunidad entera de agentes. La heterogeneidad es introducida a partir de la diversidad en los incentivos privados de los agentes. Los principales resultados de las simulaciones indican que los efectos de la información global y local pueden producir flujos informativos conflictivos para la toma de decisiones, de tal forma que el comportamiento agregado de decisión de los agentes emerge como volátil e inestable. Asimismo, los resultados indican que la interacción entre información global y local afecta la forma de la distribución del tamaño de las cascadas informativas.

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46

Rothe, Jörg. "Borda Count in Collective Decision Making: A Summary of Recent Results." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9830–36. http://dx.doi.org/10.1609/aaai.v33i01.33019830.

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Borda Count is one of the earliest and most important voting rules. Going far beyond voting, we summarize recent advances related to Borda in computational social choice and, more generally, in collective decision making. We first present a variety of well known attacks modeling strategic behavior in voting—including manipulation, control, and bribery—and discuss how resistant Borda is to them in terms of computational complexity. We then describe how Borda can be used to maximize social welfare when indivisible goods are to be allocated to agents with ordinal preferences. Finally, we illustrate the use of Borda in forming coalitions of players in a certain type of hedonic game. All these approaches are central to applications in artificial intelligence.

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47

Bavard, Sophie, Erik Stuchlý, Arkady Konovalov, and Sebastian Gluth. "Humans can infer social preferences from decision speed alone." PLOS Biology 22, no. 6 (June 20, 2024): e3002686. http://dx.doi.org/10.1371/journal.pbio.3002686.

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Humans are known to be capable of inferring hidden preferences and beliefs of their conspecifics when observing their decisions. While observational learning based on choices has been explored extensively, the question of how response times (RT) impact our learning of others’ social preferences has received little attention. Yet, while observing choices alone can inform us about the direction of preference, they reveal little about the strength of this preference. In contrast, RT provides a continuous measure of strength of preference with faster responses indicating stronger preferences and slower responses signaling hesitation or uncertainty. Here, we outline a preregistered orthogonal design to investigate the involvement of both choices and RT in learning and inferring other’s social preferences. Participants observed other people’s behavior in a social preferences task (Dictator Game), seeing either their choices, RT, both, or no information. By coupling behavioral analyses with computational modeling, we show that RT is predictive of social preferences and that observers were able to infer those preferences even when receiving only RT information. Based on these findings, we propose a novel observational reinforcement learning model that closely matches participants’ inferences in all relevant conditions. In contrast to previous literature suggesting that, from a Bayesian perspective, people should be able to learn equally well from choices and RT, we show that observers’ behavior substantially deviates from this prediction. Our study elucidates a hitherto unknown sophistication in human observational learning but also identifies important limitations to this ability.

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48

Renner, Philipp, and Karl Schmedders. "Discrete‐time dynamic principal–agent models: Contraction mapping theorem and computational treatment." Quantitative Economics 11, no. 4 (2020): 1215–51. http://dx.doi.org/10.3982/qe960.

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We consider discrete‐time dynamic principal–agent problems with continuous choice sets and potentially multiple agents. We prove the existence of a unique solution for the principal's value function only assuming continuity of the functions and compactness of the choice sets. We do this by a contraction mapping theorem and so also obtain a convergence result for the value function iteration. To numerically compute a solution for the problem, we have to solve a collection of static principal–agent problems at each iteration. As a result, in the discrete‐time setting solving the static problem is the difficult step. If the agent's expected utility is a rational function of his action, then we can transform the bi‐level optimization problem into a standard nonlinear program. The final results of our solution method are numerical approximations of the policy and value functions for the dynamic principal–agent model. We illustrate our solution method by solving variations of two prominent social planning models from the economics literature.

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Lemaire, Patrick, Laurence Arnaud, and Mireille Lecacheur. "Adults' Age-Related Differences in Adaptivity of Strategy Choices: Evidence From Computational Estimation." Psychology and Aging 19, no. 3 (2004): 467–81. http://dx.doi.org/10.1037/0882-7974.19.3.467.

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50

Lamba, Amrita, Michael J. Frank, and Oriel FeldmanHall. "Anxiety Impedes Adaptive Social Learning Under Uncertainty." Psychological Science 31, no. 5 (April 28, 2020): 592–603. http://dx.doi.org/10.1177/0956797620910993.

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Very little is known about how individuals learn under uncertainty when other people are involved. We propose that humans are particularly tuned to social uncertainty, which is especially noisy and ambiguous. Individuals exhibiting less tolerance for uncertainty, such as those with anxiety, may have greater difficulty learning in uncertain social contexts and therefore provide an ideal test population to probe learning dynamics under uncertainty. Using a dynamic trust game and a matched nonsocial task, we found that healthy subjects ( n = 257) were particularly good at learning under negative social uncertainty, swiftly figuring out when to stop investing in an exploitative social partner. In contrast, subjects with anxiety ( n = 97) overinvested in exploitative partners. Computational modeling attributed this pattern to a selective reduction in learning from negative social events and a failure to enhance learning as uncertainty rises—two mechanisms that likely facilitate adaptive social choice.

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