2,561 research outputs found
Game theory of mind
This paper introduces a model of ‘theory of mind’, namely, how we represent the intentions and goals of others to optimise our mutual interactions. We draw on ideas from optimum control and game theory to provide a ‘game theory of mind’. First, we consider the representations of goals in terms of value functions that are prescribed by utility or rewards. Critically, the joint value functions and ensuing behaviour are optimised recursively, under the assumption that I represent your value function, your representation of mine, your representation of my representation of yours, and so on ad infinitum. However, if we assume that the degree of recursion is bounded, then players need to estimate the opponent's degree of recursion (i.e., sophistication) to respond optimally. This induces a problem of inferring the opponent's sophistication, given behavioural exchanges. We show it is possible to deduce whether players make inferences about each other and quantify their sophistication on the basis of choices in sequential games. This rests on comparing generative models of choices with, and without, inference. Model comparison is demonstrated using simulated and real data from a ‘stag-hunt’. Finally, we note that exactly the same sophisticated behaviour can be achieved by optimising the utility function itself (through prosocial utility), producing unsophisticated but apparently altruistic agents. This may be relevant ethologically in hierarchal game theory and coevolution
Active Sensing as Bayes-Optimal Sequential Decision Making
Sensory inference under conditions of uncertainty is a major problem in both
machine learning and computational neuroscience. An important but poorly
understood aspect of sensory processing is the role of active sensing. Here, we
present a Bayes-optimal inference and control framework for active sensing,
C-DAC (Context-Dependent Active Controller). Unlike previously proposed
algorithms that optimize abstract statistical objectives such as information
maximization (Infomax) [Butko & Movellan, 2010] or one-step look-ahead accuracy
[Najemnik & Geisler, 2005], our active sensing model directly minimizes a
combination of behavioral costs, such as temporal delay, response error, and
effort. We simulate these algorithms on a simple visual search task to
illustrate scenarios in which context-sensitivity is particularly beneficial
and optimization with respect to generic statistical objectives particularly
inadequate. Motivated by the geometric properties of the C-DAC policy, we
present both parametric and non-parametric approximations, which retain
context-sensitivity while significantly reducing computational complexity.
These approximations enable us to investigate the more complex problem
involving peripheral vision, and we notice that the difference between C-DAC
and statistical policies becomes even more evident in this scenario.Comment: Scheduled to appear in UAI 201
The value of relationships : evidence from a supply shock to Kenyan rose exports
This paper provides evidence on the importance of reputation, intended as beliefs buyers hold about seller's reliability, in the context of the Kenyan rose export sector. A model of reputation and relational contracting is developed and tested. We show that 1) the value of the relationship increases with the age of the relationship; 2) during an exogenous negative supply shock sellers prioritize relationships consistently with the predictions of the model; and 3) reliability at the time of the shock positively correlates with future survival and relationship
value. Models exclusively focussing on enforcement or insurance considerations cannot account for the evidence
Tasks for Agent-Based Negotiation Teams:Analysis, Review, and Challenges
An agent-based negotiation team is a group of interdependent agents that join
together as a single negotiation party due to their shared interests in the
negotiation at hand. The reasons to employ an agent-based negotiation team may
vary: (i) more computation and parallelization capabilities, (ii) unite agents
with different expertise and skills whose joint work makes it possible to
tackle complex negotiation domains, (iii) the necessity to represent different
stakeholders or different preferences in the same party (e.g., organizations,
countries, and married couple). The topic of agent-based negotiation teams has
been recently introduced in multi-agent research. Therefore, it is necessary to
identify good practices, challenges, and related research that may help in
advancing the state-of-the-art in agent-based negotiation teams. For that
reason, in this article we review the tasks to be carried out by agent-based
negotiation teams. Each task is analyzed and related with current advances in
different research areas. The analysis aims to identify special challenges that
may arise due to the particularities of agent-based negotiation teams.Comment: Engineering Applications of Artificial Intelligence, 201
Optimal project rejection and new firm start-ups
Entrants are typically found to be more innovative than incumbent firms. Furthermore, these innovative ideas often originate with established firms in the industry. Therefore, the established firm and the start-up firm seem to select different types of projects. We claim that this is the consequence of their optimal project allocation mechanism, which depends on their comparative advantage. The start-up firm may seem more "innovative" than the established firm because the comparative advantage of the start-up firm is to commercialize "innovative" projects, i.e. projects that do not fit with the established firms' existing assets. Our model integrates various facts found in the industrial organization literature about the entry rate, firm focus, firm growth, industry growth and innovation. We also obtain some counter-intuitive results, such as that a reduction in the cost of start-ups may actually slow down start-ups, or that the firm may voluntarily give away the property rights to the inventions discovered within the firmManagement; Innovation management;
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