A simple evolutionary game with feedback between perception and reality

We study an evolutionary game of chance in which the probabilities for different outcomes (e.g., heads or tails) depend on the amount wagered on those outcomes. The game is perhaps the simplest possible probabilistic game in which perception affects reality. By varying the `reality map', which relates the amount wagered to the probability of the outcome, it is possible to move continuously from a purely objective game in which probabilities have no dependence on wagers, to a purely subjective game in which probabilities equal the amount wagered. The reality map can reflect self-reinforcing strategies or self-defeating strategies. In self-reinforcing games, rational players can achieve increasing returns and manipulate the outcome probabilities to their advantage; consequently, an early lead in the game, whether acquired by chance or by strategy, typically gives a persistent advantage. We investigate the game both in and out of equilibrium and with and without rational players. We introduce a method of measuring the inefficiency of the game and show that in the large time limit the inefficiency decreases slowly in its approach to equilibrium as a power law with an exponent between zero and one, depending on the subjectivity of the game.Comment: 11 pages, 6 figure

The Reality Game

We introduce an evolutionary game with feedback between perception and reality, which we call the reality game. It is a game of chance in which the probabilities for different objective outcomes (e.g., heads or tails in a coin toss) depend on the amount wagered on those outcomes. By varying the `reality map', which relates the amount wagered to the probability of the outcome, it is possible to move continuously from a purely objective game in which probabilities have no dependence on wagers to a purely subjective game in which probabilities equal the amount wagered. We study self-reinforcing games, in which betting more on an outcome increases its odds, and self-defeating games, in which the opposite is true. This is investigated in and out of equilibrium, with and without rational players, and both numerically and analytically. We introduce a method of measuring the inefficiency of the game, similar to measuring the magnitude of the arbitrage opportunities in a financial market. We prove that convergence to equilibrium is is a power law with an extremely slow rate of convergence: The more subjective the game, the slower the convergence.Comment: 21 pages, 5 figure

The reality game

We introduce an evolutionary game with feedback between perception and reality, which we call the reality game. It is a game of chance in which the probabilities for different objective outcomes (e.g. heads or tails in a coin toss) depend on the amount wagered on those outcomes. By varying the 'reality map', which relates the amount wagered to the probability of the outcome, it is possible to move continuously from a purely objective game in which probabilities have no dependence on wagers to a purely subjective game in which probabilities equal the amount wagered. We study self-reinforcing games, in which betting more on an outcome increases its odds, and self-defeating games, in which the opposite is true. This is investigated in and out of equilibrium, with and without rational players, and both numerically and analytically. We introduce a method of measuring the inefficiency of the game, similar to measuring the magnitude of the arbitrage opportunities in a financial market. We prove that the inefficiency converges to equilibrium as a power law with an extremely slow rate of convergence: the more subjective the game, the slower the convergence.Financial markets Evolutionary games Information theory Arbitrage Market efficiency Beauty contests Noise trader models Market reflexivity

The protection of urban areas from surface wastewater pollutions

In this paper it considered the problem of collection, treatment and discharge into waters of rain and melted wastewater. To reduce the load on the combined sewer system, there are engineering solutions collect rain and melt water for use in the irrigation of lawns and green spaces. Research carried out at the department “Water supply and sanitation”, (Russia), confirm the high pollution concentrations of meltwater and rainfall in urban arias. Series of measurements of heavy metal in rainwater runoff carried out in Hungary demonstrates clearly the differences in concentrations in the function of distance from the edge of the road. Also differences are introduced between pollution concentrations in runoff water from within and outside urban traffic roads. The quality of snow cover, forming meltwater is observed to be changing in dependence on roadway location. Quality characteristics of surface runoff and its sediments can be effectively improved with super-high frequency radiation (SHF) treatment which is presented in this paper

The protection of urban areas from surface wastewater pollutions

In this paper it considered the problem of collection, treatment and discharge into waters of rain and melted wastewater. To reduce the load on the combined sewer system, there are engineering solutions collect rain and melt water for use in the irrigation of lawns and green spaces. Research carried out at the department “Water supply and sanitation”, (Russia), confirm the high pollution concentrations of meltwater and rainfall in urban arias. Series of measurements of heavy metal in rainwater runoff carried out in Hungary demonstrates clearly the differences in concentrations in the function of distance from the edge of the road. Also differences are introduced between pollution concentrations in runoff water from within and outside urban traffic roads. The quality of snow cover, forming meltwater is observed to be changing in dependence on roadway location. Quality characteristics of surface runoff and its sediments can be effectively improved with super-high frequency radiation (SHF) treatment which is presented in this paper

Predicting COVID-19 and pneumonia complications from admission texts

In this paper we present a novel approach to risk assessment for patients hospitalized with pneumonia or COVID-19 based on their admission reports. We applied a Longformer neural network to admission reports and other textual data available shortly after admission to compute risk scores for the patients. We used patient data of multiple European hospitals to demonstrate that our approach outperforms the Transformer baselines. Our experiments show that the proposed model generalises across institutions and diagnoses. Also, our method has several other advantages described in the paper