Bargaining and decision making for rational agents

2017 ◽  
Author(s):  
Andrey Meshalkin
Keyword(s):  
Decision Making ◽  
Rational Agents

scholarly journals Quantum-Like Cognition and Rationality: Biological and Artificial Intelligence Systems

2021 ◽  
Author(s):  
Andrei Khrennikov
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Quantum Information ◽  
Quantum Information Processing ◽  
Bayesian Probability ◽  
Total Probability ◽  
Rational Agents ◽  
Advantages And Disadvantages ◽  
Political Sciences ◽  
Artifical Intelligence

This is a short introductory review on quantum-like modeling of cognition with applications to decision making and rationality. The aim of the review is twofold: a) to present briefly the apparatus of quantum information and probability theory useful for such modeling; b) to motivate applications of this apparatus in cognitive studies and artifical intelligence, psychology, decision making, social and political sciences. We define quantum rationality as decision making that is based on quantum information processing. Quantumly and classically rational agents behaves differently. A quantum-like agent can violate the Savage Sure Thing Principle, the Aumann theorem on impossibility of agreeing to disagree.Such an agent violates the basic laws of classical probability, e.g., the law of total probability and the Bayesian probability inference. In some contexts, ``irrational behavior'' (from the viewpoint of classical theory of rationality) can be profitable, especially for agents who are overloaded by a variety of information flows. Quantumly rational agents can save a lot of information processing resources. At the same time, this sort of rationality is the basis for quantum-like socio-political engineering, e.g., social laser. This rationality plays the important role in the process of decision making not only by biosystems, but even by AI-systems. The latter equipped with quantum(-like) information processors would behave irrationally, from the classical viewpoint. As for biosystems, quantum rational behavior of AI-systems has its advantages and disadvantages. Finally, we point out that quantum-like information processing in AI-systems can be based on classical physical devices, e.g., classical digital or analog computers.

scholarly journals Comparative analysis of the todim method adherence to prospect theory

2021 ◽  
Vol 12 (7) ◽  
pp. 1935-1947
Author(s):  
Alexandre Leoneti ◽  
Luiz Flávio Autran Monteiro Gomes
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Prospect Theory ◽  
Paired Comparison ◽  
Multicriteria Decision Aid ◽  
Rational Agents ◽  
Multicriteria Decision ◽  
Multicriteria Method ◽  
Todim Method ◽  
The Mathematical Model

Prospect Theory provides a broad and solid frame of reference for modeling the decision making of rational agents. In the early 1990s, the structure of Prospect Theory was used to propose a method to aid a multicriteria decision based on the process of paired comparison. The research reported in this article has empirically assessed the adherence of the mathematical model of the original TODIM method, together with its variations available in the literature, to Prospect Theory and compared them with a multicriteria method that does not use that theory. From a comparative analysis, it was realized that the different variations of the TODIM method regarding the incorporation of Prospect Theory’s rationality within the context of Multicriteria Decision Aid still do not bring the benefits of an already consolidated theory to the context of decision-making aid. Thus, it is suggested that further studies be conducted to improve the adherence of Prospect Theory within the structure of the TODIM method, so that the benefits of a consolidated theory of decision lead to better results, notably from the perspective of using the method for the purposes of forecast.

Agent-Based Modelnig with Boundedly Rational Agents

2007 ◽  
pp. 225-245 ◽  
Author(s):  
E. Ebenhoh
Keyword(s):  
Experimental Data ◽  
Decision Making ◽  
Model Building ◽  
Theoretical Concept ◽  
Agent Based Modeling ◽  
Modeling Framework ◽  
Agent Based ◽  
Rational Agents ◽  
Decision Making Processes ◽  
Participatory Model

This chapter introduces an agent-based modeling framework for reproducing micro behavior in economic experiments. It gives an overview of the theoretical concept which forms the foundation of the framework as well as short descriptions of two exemplary models based on experimental data. The heterogeneous agents are endowed with a number of attributes like cooperativeness and employ more or less complex heuristics during their decision-making processes. The attributes help to distinguish between agents, and the heuristics distinguish between behavioral classes. Through this design, agents can be modeled to behave like real humans and their decision making is observable and traceable, features that are important when agent-based models are to be used in collaborative planning or participatory model-building processes.

scholarly journals Optimal use of simplified social information in sequential decision-making

2021 ◽  
Author(s):  
Richard P. Mann
Keyword(s):  
Decision Making ◽  
Optimal Policy ◽  
Social Information ◽  
Sequential Decision ◽  
Hybrid Strategy ◽  
Rational Agents ◽  
Full Sequence ◽  
The Social ◽  
The Cost ◽  
Social Decision Making

AbstractSocial animals can improve their decisions by attending to the choices made by others. The rewards gained by attending to this social information must be balanced against the costs of obtaining and processing it. Previous work has investigated the behaviour of rational agents that respond optimally to a full sequence of prior decisions. However, such full sequences are potentially difficult to perceive and costly to process. As such, real animals are likely to rely on simpler forms of information when making decisions, which in turn will affect the social behaviour they exhibit. In this paper I derive the optimal policy for rational agents responding to specific simplified forms of social information. I show how the behaviour of agents attending to the total aggregate number of previous choices differs from those attending to more dynamic information provided by the most recent prior decision, and I propose a hybrid strategy that incorporates both information sources to give a highly accurate approximation to the optimal policy with the full sequence. Finally I analyse the evolutionary stability of each strategy depending on the cost of cognition and perception, showing that a hybrid strategy dominates when this cost is low but non-zero, while attending to the most recent decision is dominant when costs are high. These results show that agents can employ highly effective social decision-making rules without requiring unrealistic cognitive capacities, and point to likely ecological variation in the social information different animals attend to.

scholarly journals Optimal use of simplified social information in sequential decision-making

2021 ◽  
Vol 18 (179) ◽  
pp. 20210082
Author(s):  
Richard P. Mann
Keyword(s):  
Decision Making ◽  
Optimal Policy ◽  
Social Information ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Hybrid Strategy ◽  
Rational Agents ◽  
The Social ◽  
Social Decision Making ◽  
Prior Decision

Social animals can improve their decisions by attending to those made by others. The benefit of this social information must be balanced against the costs of obtaining and processing it. Previous work has focused on rational agents that respond optimally to a sequence of prior decisions. However, full decision sequences are potentially costly to perceive and process. As such, animals may rely on simpler social information, which will affect the social behaviour they exhibit. Here, I derive the optimal policy for agents responding to simplified forms of social information. I show how the behaviour of agents attending to the aggregate number of previous choices differs from those attending to just the most recent prior decision, and I propose a hybrid strategy that provides a highly accurate approximation to the optimal policy with the full sequence. Finally, I analyse the evolutionary stability of each strategy, showing that the hybrid strategy dominates when cognitive costs are low but non-zero, while attending to the most recent decision is dominant when costs are high. These results show that agents can employ highly effective social decision-making rules without requiring unrealistic cognitive capacities, and indicate likely ecological variation in the social information different animals attend to.

scholarly journals Computational Goals, Values and Decision-Making

2020 ◽  
Vol 26 (5) ◽  
pp. 2487-2495 ◽  
Author(s):  
Louise A. Dennis
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Utility Function ◽  
Expected Utility ◽  
Engineering Ethics ◽  
Rational Agent ◽  
Autonomous Agent ◽  
Science And Engineering ◽  
Rational Agents ◽  
Do So

Abstract Considering the popular framing of an artificial intelligence as a rational agent that always seeks to maximise its expected utility, referred to as its goal, one of the features attributed to such rational agents is that they will never select an action which will change their goal. Therefore, if such an agent is to be friendly towards humanity, one argument goes, we must understand how to specify this friendliness in terms of a utility function. Wolfhart Totschnig (Fully Autonomous AI, Science and Engineering Ethics, 2020), argues in contrast that a fully autonomous agent will have the ability to change its utility function and will do so guided by its values. This commentary examines computational accounts of goals, values and decision-making. It rejects the idea that a rational agent will never select an action that changes its goal but also argues that an artificial intelligence is unlikely to be purely rational in terms of always acting to maximise a utility function. It nevertheless also challenges the idea that an agent which does not change its goal cannot be considered fully autonomous. It does agree that values are an important component of decision-making and explores a number of reasons why.

scholarly journals Concluding Thoughts

2021 ◽  
pp. 149-154
Author(s):  
Neil Levy
Keyword(s):  
Decision Making ◽  
Higher Order ◽  
Scarce Resource ◽  
Ecological Rationality ◽  
Good Information ◽  
Rational Agents ◽  
Self Image ◽  
The Enlightenment ◽  
Human Decision ◽  
Higher Order Evidence

This brief concluding chapter draws the threads of the previous chapters together. Previous work on human decision-making has tended to conclude that rationality is a scarce resource and most cognition is arational or irrational. Pushback against this view has come from proponents of ecological rationality. They concede, in effect, that our decision-making is irrational, inasmuch as it fails to respond to good information, but argue that it is rational in a broader sense: we better achieve our epistemic goals by believing arationally. This chapter argues that the evidence surveyed in the previous chapters shows that this is false: we respond rationally to the higher-order evidence we’re presented with, and there’s therefore no need to appeal to ecological rationality to defend our self-image as rational agents. Once we recognize the pervasiveness of higher-order evidence, we can vindicate something very like the Enlightenment picture of ourselves as rational animals.

scholarly journals Comparative Analysis of Iterative Processes of Equilibrium Calculation in Oligopoly Models

2020 ◽  
pp. 63-67
Author(s):  
G.I. Algazin ◽  
D.G. Algazina
Keyword(s):  
Decision Making ◽  
Dynamic Decision Making ◽  
Linear Functions ◽  
Product Model ◽  
Market Prices ◽  
Current Output ◽  
Rational Agents ◽  
Optimal Behavior ◽  
Marginal Costs ◽  
Decision Making Processes

The problem of choosing the optimal behavior of agents in the classic one-product model of the competitive market under linear functions of demand and costs of agents is considered. The dynamic decision-making processes under conditions of uncertainty of decision making by competitors, performed as repeatable static games within a range of admissible answers, are investigated. The analysis targets processes with rational agents using approaches of multi-step reflexive games and models of collective behavior to refine the solutions while observing the current market prices of goods. The processes are distinguished by choosing the current targets: in one case, the agents choose their current output as the current targets when refining the solutions; in another case, the current targets are perceptions of agents about the current equilibrium marginal cost of other agents. It is shown that for oligopolies with Cournot and/ or Stackelberg competition, processes with agents focused on the expected optimum output are more preferable than processes with agents focused on their perceptions of the equilibrium marginal costs of competitors because there are greater opportunities for finding equilibrium states.

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

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