Economic efficiency of livestock organic waste processing into biонumus

The processing of organic livestock waste into biohumus is one of the direction areas in agribusiness, which contributes to the efficient resource allocation involved while achieving a high level of greening of production. The article presents the results of the study of economic efficiency of extensive form of vermiculture, which is characterized by minimal start-up investment, ease of organization and accessibility for the vast majority of livestock producers, including small farms, as it does not involve additional premises. Compared to intensive technology, which requires indoor heated premises to organize the production of biohumus throughout the year. The need for fixed and working capital for the organization of organic livestock waste processing into compost has been determined. Planning and analysis of costs for the organization of production and sale of vermiculture products. The main technical parameters of the organization of the production process are determined, which will ensure high efficiency of this type of business. Based on the definition of the main indicators of economic efficiency, the expediency of introducing an extensive form of organic livestock waste processing into biohumus is substantiated. According to the research results, it is established that the organization of extensive technology of processing organic livestock waste on compost is more appropriate for small farms in the livestock industry with a limited investment budget. As about 6 million UAH is needed to organize the processing of 2.400 tons of livestock waste. (in 2021 prices) of advanced capital, which is 30–40 % less compared to intensive technology of similar scale. The projected payback period of extensive vermiculture technology will be three years when it reaches 25 % of the level of profitability of sales. Also, the organization of extensive technology for processing animal waste into biohumus requires less time compared to intensive.

Partial Order Games

We introduce a non-cooperative game model in which players’ decision nodes are partially ordered by a dependence relation, which directly captures informational dependencies in the game. In saying that a decision node v is dependent on decision nodes v1,…,vk, we mean that the information available to a strategy making a choice at v is precisely the choices that were made at v1,…,vk. Although partial order games are no more expressive than extensive form games of imperfect information (we show that any partial order game can be reduced to a strategically equivalent extensive form game of imperfect information, though possibly at the cost of an exponential blowup in the size of the game), they provide a more natural and compact representation for many strategic settings of interest. After introducing the game model, we investigate the relationship to extensive form games of imperfect information, the problem of computing Nash equilibria, and conditions that enable backwards induction in this new model.

Do supermajority rules really deter extremism? the role of electoral competition

This study investigates how supermajority rules in a legislature affect electoral competition. We construct an extensive-form game wherein parties choose policy platforms in an election. Post election, the policy is determined based on a legislative voting rule. At symmetric equilibrium, supermajority rules induce divergence of policy platforms if and only if the parties are sufficiently attached to their preferred platform. Thus, supermajority rules may not always lead to moderate policies once electoral competition is considered.

Paternalism: A Flawed Basis for Liberty-limiting Policies?

This article discusses coercive paternalism, a concept of liberty-limitations that has gained significant attention in recent decades. In opposition to the libertarian type of paternalism proposed by the well-known ‘Nudgers’ Richard H. Thaler and Cass R. Sunstein (2008), Sarah Conly (2013) advocates coercive interventions in Against Autonomy: Justifying Coercive Paternalism. Her influential work serves as a basis for scrutinizing the validity of coercive paternalism’s presuppositions as well as the internal coherence of the concept. Following the fundamental groundwork of especially Joel Feinberg and Gerald Dworkin, arguments against coercive paternalism are evaluated. They include the reciprocal (rather than unilateral) relationship between the ‘present self’ and the ‘future self’ in the paternalist’s account, the questionable legitimacy of punishment for self-harming behaviour and of coercion in general, the challenges of so-called ‘perfectionism’ and slippery-slopes, as well as a misconception about the alleged lack of rationality that serves as a justification for coercive paternalism. The article concludes by suggesting that – given the flaws of the concept – it may be reasonable to favour soft paternalism à la John Stuart Mill based on the harm principle over Conly’s proposal for a more extensive form of coercive paternalism.

CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Space

In many real-world scenarios, a team of agents must coordinate with each other to compete against an opponent. The challenge of solving this type of game is that the team's joint action space grows exponentially with the number of agents, which results in the inefficiency of the existing algorithms, e.g., Counterfactual Regret Minimization (CFR). To address this problem, we propose a new framework of CFR: CFR-MIX. Firstly, we propose a new strategy representation that represents a joint action strategy using individual strategies of all agents and a consistency relationship to maintain the cooperation between agents. To compute the equilibrium with individual strategies under the CFR framework, we transform the consistency relationship between strategies to the consistency relationship between the cumulative regret values. Furthermore, we propose a novel decomposition method over cumulative regret values to guarantee the consistency relationship between the cumulative regret values. Finally, we introduce our new algorithm CFR-MIX which employs a mixing layer to estimate cumulative regret values of joint actions as a non-linear combination of cumulative regret values of individual actions. Experimental results show that CFR-MIX outperforms existing algorithms on various games significantly.

A Game-Theoretic Account of Responsibility Allocation

When designing or analyzing multi-agent systems, a fundamental problem is responsibility ascription: to specify which agents are responsible for the joint outcome of their behaviors and to which extent. We model strategic multi-agent interaction as an extensive form game of imperfect information and define notions of forward (prospective) and backward (retrospective) responsibility. Forward responsibility identifies the responsibility of a group of agents for an outcome along all possible plays, whereas backward responsibility identifies the responsibility along a given play. We further distinguish between strategic and causal backward responsibility, where the former captures the epistemic knowledge of players along a play, while the latter formalizes which players – possibly unknowingly – caused the outcome. A formal connection between forward and backward notions is established in the case of perfect recall. We further ascribe quantitative responsibility through cooperative game theory. We show through a number of examples that our approach encompasses several prior formal accounts of responsibility attribution.

Decentralized No-regret Learning Algorithms for Extensive-form Correlated Equilibria (Extended Abstract)

The existence of uncoupled no-regret learning dynamics converging to correlated equilibria in normal-form games is a celebrated result in the theory of multi-agent systems. Specifically, it has been known for more than 20 years that when all players seek to minimize their internal regret in a repeated normal-form game, the empirical frequency of play converges to a normal-form correlated equilibrium. Extensive-form games generalize normal-form games by modeling both sequential and simultaneous moves, as well as imperfect information. Because of the sequential nature and the presence of private information, correlation in extensive-form games possesses significantly different properties than in normal-form games. The extensive-form correlated equilibrium (EFCE) is the natural extensive-form counterpart to the classical notion of correlated equilibrium in normal-form games. Compared to the latter, the constraints that define the set of EFCEs are significantly more complex, as the correlation device ({\em a.k.a.} mediator) must take into account the evolution of beliefs of each player as they make observations throughout the game. Due to this additional complexity, the existence of uncoupled learning dynamics leading to an EFCE has remained a challenging open research question for a long time. In this article, we settle that question by giving the first uncoupled no-regret dynamics which provably converge to the set of EFCEs in n-player general-sum extensive-form games with perfect recall. We show that each iterate can be computed in time polynomial in the size of the game tree, and that, when all players play repeatedly according to our learning dynamics, the empirical frequency of play after T game repetitions is guaranteed to be a O(T^-1/2)-approximate EFCE with high probability, and an EFCE almost surely in the limit.

Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play

Securing networked infrastructures is important in the real world. The problem of deploying security resources to protect against an attacker in networked domains can be modeled as Network Security Games (NSGs). Unfortunately, existing approaches, including the deep learning-based approaches, are inefficient to solve large-scale extensive-form NSGs. In this paper, we propose a novel learning paradigm, NSG-NFSP, to solve large-scale extensive-form NSGs based on Neural Fictitious Self-Play (NFSP). Our main contributions include: i) reforming the best response (BR) policy network in NFSP to be a mapping from action-state pair to action-value, to make the calculation of BR possible in NSGs; ii) converting the average policy network of an NFSP agent into a metric-based classifier, helping the agent to assign distributions only on legal actions rather than all actions; iii) enabling NFSP with high-level actions, which can benefit training efficiency and stability in NSGs; and iv) leveraging information contained in graphs of NSGs by learning efficient graph node embeddings. Our algorithm significantly outperforms state-of-the-art algorithms in both scalability and solution quality.

Use of Geothermal Exchangers for Thermoaccumulation of the Energy Generated by Climatizers Activated by Photovoltaic Sources

The use of air conditioning is growing up very much in Brazil and in the world in general. That is no longer a luxury, but a necessity, though they have heavy impacts on the consumption of electricity. The alignment between the peak thermal load and the highest solar incidence of solar energy leads the scientific community to search for machines powered by this type of energy. Energy costs are higher when the solar energy is decreasing so recommending a more powerful storage of energy. Regarding this point of view, soil is one of the oldest, universal and most extensive form of thermal energy storage. This paper shows how thermal energy storage can be used to provide air conditioning at very low costs. A simple technology is dicussed showing that it is possible to store energy underground and recover it in a very efficient way to provide thermal comfort.