Decisions with multiple simultaneous goals and uncertain causal effects

A key aspect of decision-making in a disaster response scenario is the capability to evaluate multiple and simultaneously perceived goals. Current competing approaches to build decision-making agents are either mental-state based as BDI, or founded on decision-theoretic models as MDP. The BDI chooses heuristically among several goals and the MDP searches for a policy to achieve a specific goal. In this paper we develop a preferences model to decide among multiple simultaneous goals. We propose a pattern, which follows a decision-theoretic approach, to evaluate the expected causal effects of the observable and non-observable aspects that inform each decision. We focus on yes-or-no (i.e., pursue or ignore a goal) decisions and illustrate the proposal using the RoboCupRescue simulation environment

Fuzzy Logic Based Negotiation in E-Commerce

The evolution of multi-agent system (MAS) presents new challenges in computer science and software engineering. A particularly challenging problem is the design of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is dealing with negotiation in e-commerce: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, we present a fuzzy logic-based negotiation approach to automate multi-issue bilateral negotiation in e-marketplaces. In such frameworks issues to negotiate on can be multiple, interrelated, and may not be fixed in advance. Therefore, we use fuzzy inference system to model relations among issues and to allow agents express their preferences on them. We focus on settings where agents have limited or uncertain information, ruling them out from making optimal decisions. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs then applying logic (by using fuzzy logic) over these reasons can enable agents to refine their decisions and consequently reach better agreements. I refer to this form of negotiation as: Fuzzy logic based negotiation in e-commerce. The contributions of the thesis begin with the use of fuzzy logic to design a reasoning model through which negotiation tactics and strategy are expressed throughout the process of negotiation. Then, an exploration of the differences between this approach and the more traditional bargaining-based approaches is presented. Strategic issues are then explored and a methodology for designing negotiation strategies is developed. Finally, the applicability of the framework is simulated using MATLAB toolbox

Fuzzy Logic Based Negotiation in E-Commerce

The evolution of multi-agent system (MAS) presents new challenges in computer science and software engineering. A particularly challenging problem is the design of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is dealing with negotiation in e-commerce: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, we present a fuzzy logic-based negotiation approach to automate multi-issue bilateral negotiation in e-marketplaces. In such frameworks issues to negotiate on can be multiple, interrelated, and may not be fixed in advance. Therefore, we use fuzzy inference system to model relations among issues and to allow agents express their preferences on them. We focus on settings where agents have limited or uncertain information, ruling them out from making optimal decisions. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs then applying logic (by using fuzzy logic) over these reasons can enable agents to refine their decisions and consequently reach better agreements. I refer to this form of negotiation as: Fuzzy logic based negotiation in e-commerce. The contributions of the thesis begin with the use of fuzzy logic to design a reasoning model through which negotiation tactics and strategy are expressed throughout the process of negotiation. Then, an exploration of the differences between this approach and the more traditional bargaining-based approaches is presented. Strategic issues are then explored and a methodology for designing negotiation strategies is developed. Finally, the applicability of the framework is simulated using MATLAB toolbox

Extending Kolkata Paise Restaurant Problem to Dynamic Matching in Mobility Markets

In mobility markets – especially vehicle for hire markets – drivers offer individual transportation by car to customers. Drivers individually decide where to go to pick up customers to increase their own utilization (probability of carrying a customer) and utility (profit). The utility drivers retrieve from customers comprises both costs of driving to another location and the revenue from carrying a customer and is thus not shared between different drivers. In this thesis, I present the Vehicle for Hire Problem (VFHP) as a generalization of the Kolkata Paise Restaurant Problem (KPRP) to evaluate different strategies for drivers in vehicle for hire markets. The KPRP is a multi-round game model presented by Chakrabarti et al. (2009) in which daily laborers constitute agents and restaurants constitute resources. All agents decide simultaneously, but independently where to eat. Every restaurant can cater only one agent and agents cannot divert to other resources if their first choice is overcrowded. The number of agents equals the number of resources. Also, there is a ranking of restaurants all agents agree upon, and no two resources yield the same utility. The VFHP relaxes assumptions on capacity and utility: Resources (customers) are grouped in districts, agents (drivers) can redirect to other resources in the same district. As the distance between agent and resource reduces the agent’s utility and the location is not identical for all agents, the utility of a given resource is not identical for all agents. To study the impact of the different assumptions, I build four different model variants: Individual Preferences (IP) replaces the shared utility of the KPRP with uniformly distributed utilities per agent. The Mixed Preferences (MP) model variant uses the utility assumption of the VFHP, but the capacity of all districts remains 1. The Individual Preferences with Multiple Customers per District (IPMC) model variant groups customers in districts, and uses the uniform utilities introduced in the IP model variant. Mixed Preferences and Multiple Customers per District (MPMC) implements all assumptions of the VHFP. In this thesis, I study different strategies for the KPRP and all variants of the VFHP to build a foundation for an incentive scheme for dynamic matching in mobility markets. The strategies comprise history-dependent and utility-dependent strategies. In history-dependent strategies, agents incorporate their previous decisions and the utilization of resources in previous iterations in their decision. Agents adapting utility-dependent strategies choose the resource offering the highest utility with a given probability. Keywords: vehicle for hire markets; distributed decision making; agent-based modelling; congestion game; limited rationalit

Three Essays on Information Economics

This dissertation is a collection of three essays on information economics. The first essay, “Budget Constraint and Information Transmission in Multidimensional Decision Making,” illustrates how a constraint on a receiver’s actions impedes information transmission from multiple senders. The constraint causes an endogenous conflict of interest between the senders and the receiver, preventing truthful revelation. Nevertheless, information can be at least partially transmitted in terms of grids in perfect Bayesian equilibrium. The second essay, “Delegation and Retention of Authority in Organizations under Con- strained Decision Making,” analyzes the relationship between constraints on decision making and optimal decision making structure–centralized or decentralized decision making. Con- straints on feasible decisions induce a conflict of interest between a principal and agents even if they have common preferences. The centralized decision is beneficial to the principal when the constraints weakly restrictive. However, delegation can more than compensate the principal for loss of control by exploiting the agents’ information when different prior beliefs disrupt information revelation or the agents have difference preferences. The third essay, “Tenure Reform and Quality Gap between Schools,” discusses tenure reform for primary and secondary education in the United State from a game-theoretical point of view. To analyze the effect of the reform, a continuing contract (tenure) is compared with a non-continuing contract (non-tenure) based on performance evaluation. The welfare is improved after the reform, but the gap between a good and bad school becomes wider. This increased gap is caused by a unilateral transfer of qualified teachers from the good school to bad school

Colour reverse learning and animal personalities: the advantage of behavioural diversity assessed with agent-based simulations

Foraging bees use colour cues to help identify rewarding from unrewarding flowers, but as conditions change, bees may require behavioural flexibility to reverse their learnt preferences. Perceptually similar colours are learnt slowly by honeybees and thus potentially pose a difficult task to reverse-learn. Free-flying honeybees (N = 32) were trained to learn a fine colour discrimination task that could be resolved at ca. 70% accuracy following extended differential conditioning, and were then tested for their ability to reverse-learn this visual problem multiple times. Subsequent analyses identified three different strategies: ‘Deliberative-decisive’ bees that could, after several flower visits, decisively make a large change to learnt preferences; ‘Fickle- circumspect’ bees that changed their preferences by a small amount every time they encountered evidence in their environment; and ‘Stay’ bees that did not change from their initially learnt preference. The next aim was to determine if there was any advantage to a colony in maintaining bees with a variety of decision-making strategies. To understand the potential benefits of the observed behavioural diversity agent-based computer simulations were conducted by systematically varying parameters for flower reward switch oscillation frequency, flower handling time, and fraction of defective ‘target’ stimuli. These simulations revealed that when there is a relatively high frequency of reward reversals, fickle-circumspect bees are more efficient at nectar collection. However, as the reward reversal frequency decreases the performance of deliberative-decisive bees becomes most efficient. These findings show there to be an evolutionary benefit for honeybee colonies with individuals exhibiting these different strategies for managing resource change. The strategies have similarities to some complex decision-making processes observed in humans, and algorithms implemented in artificial intelligence systems

(WP 2016-03) Economics, Neuroeconomics, and the Problem of Identity

This paper reviews the debate in economics over neuroeconomics’ contribution to economics. It distinguishes majority and minority views, argues that this debate has been framed by mainstream economics’ conception of itself as an isolated science, and argues that this framing has put off the agenda in economics issues such as individual identity that are increasingly important in connection with the social and historical context of economic explanations in a changing complex world. The paper first discusses how the debate over neuroeconomics has been limited to the question of what information from other sciences might be employed in economics. It then goes on to the individual identity issue, and discusses how economics’ top-down, closed character generates a circular individual identity conception, while bottom-up, open character of psychology and neuroscience, and their continual concern with the changing relation between theory and evidence, has produced four competing individual identity conceptions in neuroeconomic research

The Emergence of Norms via Contextual Agreements in Open Societies

This paper explores the emergence of norms in agents' societies when agents play multiple -even incompatible- roles in their social contexts simultaneously, and have limited interaction ranges. Specifically, this article proposes two reinforcement learning methods for agents to compute agreements on strategies for using common resources to perform joint tasks. The computation of norms by considering agents' playing multiple roles in their social contexts has not been studied before. To make the problem even more realistic for open societies, we do not assume that agents share knowledge on their common resources. So, they have to compute semantic agreements towards performing their joint actions. %The paper reports on an empirical study of whether and how efficiently societies of agents converge to norms, exploring the proposed social learning processes w.r.t. different society sizes, and the ways agents are connected. The results reported are very encouraging, regarding the speed of the learning process as well as the convergence rate, even in quite complex settings