Publications

We give a criterion of positivity for the value of a matrix game. We use the criterion to investigate conditions of positivity of value for two classes of games: payoff matrices with all elements outside of the main diagonal having the same sign and symmetric payoff matrices.

This paper introduces and analyzes a procedural egalitarian solution for nontransferable utility games. This concept is based on an egalitarian procedure in which egalitarian opportunities of coalitions are explicitly taken into account. We formulate conditions under which the new solution prescribes a core element and derive a direct expression on the class of bargaining games and the class of bankruptcy games.

Using a simplified multistage bidding model with asymmetrically informed agents, De Meyer and Saley [17] demonstrated an idea of endogenous origin of the Brownian component in the evolution of prices on stock markets: random price fluctuations may be caused by strategic randomization of “insiders.” The model is reduced to a repeated game with incomplete information. This paper presents a survey of numerous researches inspired by the pioneering publication of De Meyer and Saley.

We investigate how prescriptive and descriptive norms affect the development of corruption over time. In particular, we are interested in whether the extent of corruption converges. If it does, we study how the level at which it converges depends on the prescriptive norms in the environment in which it takes place and on the information individuals have about others’ corrupt choices, that is, on descriptive norms. In a laboratory experiment implemented in Italy, China and the Netherlands, a Gneezy-type corruption task is used, with a real-effort task. We use a Krupka-Weber elicitation method to obtain information about existing prescriptive norms with respect to corrupt behavior. To induce natural variation in descriptive norms, we vary the type of information about others’ choices. Our results show that corruption is highly contagious everywhere, that is, descriptive norms affect choices. Nevertheless, differences in the effects of descriptive norms are evident across countries. Prescriptive norms concerning bribers’ and judges’ behaviors are observed to differ across the considered subject pools. While in China and the Netherlands it is highly socially inappropriate to bribe and, if you are a decision maker, to treat unfavorably people with high efforts and low bribes, in Italy the norms are the opposite.

We compare the Egalitarian rule (aka Egalitarian Equivalent) and the Competitive rule (aka Competitive Equilibrium with Equal Incomes) to divide bads (chores). They are both welfarist: the competitive disutility profile(s) are the critical points of their Nash product on the set of efficient feasible profiles. The C rule is Envy Free, Maskin Monotonic, and has better incentives properties than the E rule. But, unlike the E rule, it can be wildly multivalued, admits no selection continuous in the utility and endowment parameters, and is harder to compute. Thus in the division of bads, unlike that of goods, no rule normatively dominates the other.

Fair division, a key concern in the design of many social institutions, has for 70 years been the subject of interdisciplinary research at the interface of mathematics, economics, and game theory. Motivated by the proliferation of moneyless transactions on the internet, the computer science community has recently taken a deep interest in fairness principles and practical division rules. The resulting literature brings a fresh concern for computational simplicity (scalable rules) and realistic implementation. In this review of the most salient fair division results of the past 30 years, I concentrate on division rules with the best potential for practical implementation. The critical design parameter is the message space that the agents must use to report their individual preferences. A simple preference domain is key both to realistic implementation and to the existence of division rules with strong normative and incentive properties. I discuss successively the one-dimensional single-peaked domain, Leontief utilities, ordinal ranking, dichotomous preferences, and additive utilities. Some of the theoretical results in the latter domain are already implemented in the user-friendly SPLIDDIT platform

We consider a setting in which agents vote to choose a fair mixture of public outcomes. The agents have dichotomous preferences: each outcome is liked or disliked by an agent. We discuss three outstanding voting rules. The Conditional Utilitarian rule, a variant of the random dictator, is strategyproof and guarantees to any group of like-minded agents an influence proportional to its size. It is easier to compute and more efficient than the familiar Random Priority rule. Its worst case (resp. average) inefficiency is provably (resp. in numerical experiments) low if the number of agents is low. The efficient Egalitarian rule protects individual agents but not coalitions. It is excludable strategyproof: I do not want to lie if I cannot consume outcomes I claim to dislike. The efficient Nash Max Product rule offers the strongest welfare guarantees to coalitions, who can force any outcome with a probability proportional to their size. But it even fails the excludable form of strategyproofness.

This research is motivated by the global warming problem, which is likely influenced by human activity. Fast-growing oil palm plantations in the tropical belt of Africa, Southeast Asia and parts of Brazil lead to significant loss of rainforest and contribute to the global warming by the corresponding decrease of carbon dioxide absorption. We propose a novel approach to monitoring of the development of such plantations based on an application of state-of-the-art Fully Convolutional Neural Networks (FCNs) to solve Semantic Segmentation Problem for Landsat imagery.

Among the bryophytes, biological growth rhythms have not yet been identified due to a lack of long‐term precision observations. Here we carry out precision field monitoring of the growth of the peat moss Sphagnum riparium using the recent geotropic curvature method. For four years, using the observation intervals of 2‐5 days, we measured 116 469 shoots and received 4 493 estimates of growth rates. We found three rhythmic growth components. The seasonal rhythm of growth has a period of about 180 days, which coincides with the seasonal temperature cycle. The circalunar growth rhythm has a period of about 29.5 days and coincides with the synodic lunar cycle. There is an acceleration near the new moon, and a slowdown in growth near the full moon. The third rhythm has a period of 7‐16 days and is always synchronized with the circalunar growth rhythm. From the models of the sums of sinusoids, we found that the total contribution of all three rhythms to the growth rate is R2 = 0.51‐0.78, and without taking into account the seasonal rhythm, R2 = 0.36‐0.42. Thus, our study gives the first data on the biological rhythms and the contribution of these rhythms to the growth process of bryophytes. We attribute the unexpectedly high contribution of rhythms to the synchronous growth of the Sphagnum mat, which is necessary to reduce the loss of moisture.

This paper is devoted to a new class of differential games with continuous updating. It is assumed that at each time instant, players have or use information about the game defined on a closed time interval. However, as the time evolves, information about the game updates, namely, there is a continuous shift of time interval, which determines the information available to players. Information about the game is the information about motion equations and payoff functions of players. For this class of games, the direct application of classical approaches to the determination of optimality principles such as Nash equilibrium is not possible. The subject of the current paper is the construction of solution concept similar to Nash equilibrium for this class of differential games and corresponding optimality conditions, in particular, modernized Hamilton-Jacobi-Bellman equations.

We propose a novel machine-learning-based approach to detect bid leakage in first-price sealed-bid auctions. We extract and analyze the data on more than 1.4 million Russian procurement auctions between 2014 and 2018. As bid leakage in each particular auction is tacit, the direct classification is impossible. Instead, we reduce the problem of bid leakage detection to Positive-Unlabeled Classification. The key idea is to regard the losing participants as fair and the winners as possibly corrupted. This allows us to estimate the prior probability of bid leakage in the sample, as well as the posterior probability of bid leakage for each specific auction. We find that at least 16% of auctions are exposed to bid leakage. Bid leakage is more likely in auctions with a higher reserve price, lower number of bidders and lower price fall, and where the winning bid is received in the last hour before the deadline.

The Gibbard–Satterthwaite theorem is a cornerstone of social choice theory, stating that an onto social choice function cannot be both strategy-proof and non-dictatorial if the number of alternatives is at least three. The Duggan–Schwartz theorem proves an analogue in the case of set-valued elections: if the function is onto with respect to singletons, and can be manipulated by neither an optimist nor a pessimist, it must have a weak dictator. However, the assumption that the function is onto with respect to singletons makes the Duggan–Schwartz theorem inapplicable to elections which necessarily select multiple winners. In this paper we make a start on this problem by considering rules which always elect exactly two winners (such as the consulship of ancient Rome). We establish that if such a consular election rule cannot be expressed as the union of two disjoint social choice functions, then strategy-proofness implies the existence of a dictator. Although we suspect that a similar result holds for k-winner rules for k>2k>2, there appear to be many obstacles to proving it, which we discuss in detail.