Preference fusion and Condorcet's Paradox under uncertainty

Facing an unknown situation, a person may not be able to firmly elicit his/her preferences over different alternatives, so he/she tends to express uncertain preferences. Given a community of different persons expressing their preferences over certain alternatives under uncertainty, to get a collective representative opinion of the whole community, a preference fusion process is required. The aim of this work is to propose a preference fusion method that copes with uncertainty and escape from the Condorcet paradox. To model preferences under uncertainty, we propose to develop a model of preferences based on belief function theory that accurately describes and captures the uncertainty associated with individual or collective preferences. This work improves and extends the previous results. This work improves and extends the contribution presented in a previous work. The benefits of our contribution are twofold. On the one hand, we propose a qualitative and expressive preference modeling strategy based on belief-function theory which scales better with the number of sources. On the other hand, we propose an incremental distance-based algorithm (using Jousselme distance) for the construction of the collective preference order to avoid the Condorcet Paradox.Comment: International Conference on Information Fusion, Jul 2017, Xi'an, Chin

A reason-based theory of rational choice

There is a surprising disconnect between formal rational choice theory and philosophical work on reasons. The one is silent on the role of reasons in rational choices, the other rarely engages with the formal models of decision problems used by social scientists. To bridge this gap, we propose a new, reason-based theory of rational choice. At its core is an account of preference formation, according to which an agent's preferences are determined by his or her motivating reasons, together with a weighing relation between di¤erent combinations of reasons. By explaining how someone's preferences may vary with changes in his or her motivating reasons, our theory illuminates the relationship between deliberation about reasons and rational choices. Although primarily positive, the theory can also help us think about how those preferences and choices ought to respond to normative reasons.rational choice theory, reasons, motivation, expected utility theory, methodology, preference formation, dynamic inconsistency

Imperfect Knowledge and Asset Price Dynamics: Modeling the Forecasting of Rational Agents, Dynamic Prospect Theory and Uncertainty Premia on Foreign Exchange.

Models using the Rational Expectations Hypothesis (REH) are widely recognized to be inconsistent with the observed behavior of premia in financial markets, as well as other features of asset price dynamics. Moreover, many reasons have been advanced as to why the REH cannot generally represent, even approximately, the expectations behavior of individually rational agents. In this paper, we develop a new model of the equilibrium premium in the foreign exchange market that replaces the REH with the Imperfect Knowledge Forecasting (IKF) framework. Because we maintain that agents must cope with imperfect knowledge and that they are not grossly irrational, our IKF approach imposes only qualitative conditions on the formation of individual forecasting models and their updating. We also develop a dynamic extension of the original formulation of Kahneman and Tversky’s prospect theory. We find that under IKF and dynamic prospect theory, the equilibrium premium on foreign exchange is positively related to the gap between the aggregate forecast of the exchange rate and its historical benchmark level. We test this implication, using survey data on the German mark-U.S. dollar exchange rate, and find that the behavior of the ex ante premium on foreign exchange is consistent with our model of the premium.exchange rates; risk premium; imperfect knowledge; individual rationality; expectations; prospect theory

Imperfect Knowledge and Asset Price Dynamics: Modeling the Forecasting of Rational Agents, Dynamic Prospect Theory and Uncertainty Premia on Foreign Exchange.

Models using the Rational Expectations Hypothesis (REH) are widely recognized to be inconsistent with the observed behavior of premia in financial markets, as well as other features of asset price dynamics. Moreover, many reasons have been advanced as to why the REH cannot generally represent, even approximately, the expectations behavior of individually rational agents. In this paper, we develop a new model of the equilibrium premium in the foreign exchange market that replaces the REH with the Imperfect Knowledge Forecasting (IKF) framework. Because we maintain that agents must cope with imperfect knowledge and that they are not grossly irrational, our IKF approach imposes only qualitative conditions on the formation of individual forecasting models and their updating. We also develop a dynamic extension of the original formulation of Kahneman and Tversky's prospect theory. We find that under IKF and dynamic prospect theory, the equilibrium premium on foreign exchange is positively related to the gap between the aggregate forecast of the exchange rate and its historical benchmark level. We test this implication, using survey data on the German mark-U.S. dollar exchange rate, and find that the behavior of the ex ante premium on foreign exchange is consistent with our model of the premium.EXCHANGE RATES; RISK PREMIUM; IMPERFECT KNOWLEDGE; INDIVIDUAL Rationality; Expectations; Prospect Theory

An Efficient Protocol for Negotiation over Combinatorial Domains with Incomplete Information

We study the problem of agent-based negotiation in combinatorial domains. It is difficult to reach optimal agreements in bilateral or multi-lateral negotiations when the agents' preferences for the possible alternatives are not common knowledge. Self-interested agents often end up negotiating inefficient agreements in such situations. In this paper, we present a protocol for negotiation in combinatorial domains which can lead rational agents to reach optimal agreements under incomplete information setting. Our proposed protocol enables the negotiating agents to identify efficient solutions using distributed search that visits only a small subspace of the whole outcome space. Moreover, the proposed protocol is sufficiently general that it is applicable to most preference representation models in combinatorial domains. We also present results of experiments that demonstrate the feasibility and computational efficiency of our approach

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

The Effect of Communicating Ambiguous Risk Information on Choice

Decision makers are frequently confronted with ambiguous risk information about activities with potential hazards. This may be a result of conflicting risk estimates from multiple sources or ambiguous risk information from a single source. The paper considers processing ambiguous risk information and its effect on the behavior of a decision maker with a-maximin expected utility preferences. The effect of imprecise risk information on behavior is related to the content of information, the decision maker’s trust in different sources of information, and his or her aversion to ambiguity.a-Maximin Expected Utility, aggregation of expert opinions, ambiguity, Knightian uncertainty, risk communication, trust in information source, Risk and Uncertainty,

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation