98,371 research outputs found
Classical Logical versus Quantum Conceptual Thought: Examples in Economics, Decision theory and Concept Theory
Inspired by a quantum mechanical formalism to model concepts and their
disjunctions and conjunctions, we put forward in this paper a specific
hypothesis. Namely that within human thought two superposed layers can be
distinguished: (i) a layer given form by an underlying classical deterministic
process, incorporating essentially logical thought and its indeterministic
version modeled by classical probability theory; (ii) a layer given form under
influence of the totality of the surrounding conceptual landscape, where the
different concepts figure as individual entities rather than (logical)
combinations of others, with measurable quantities such as 'typicality',
'membership', 'representativeness', 'similarity', 'applicability', 'preference'
or 'utility' carrying the influences. We call the process in this second layer
'quantum conceptual thought', which is indeterministic in essence, and contains
holistic aspects, but is equally well, although very differently, organized
than logical thought. A substantial part of the 'quantum conceptual thought
process' can be modeled by quantum mechanical probabilistic and mathematical
structures. We consider examples of three specific domains of research where
the effects of the presence of quantum conceptual thought and its deviations
from classical logical thought have been noticed and studied, i.e. economics,
decision theory, and concept theories and which provide experimental evidence
for our hypothesis.Comment: 14 page
Probability and Uncertainty in Economic Modeling, Second Version
Economic modeling assumes, for the most part, that agents are Bayesian, that is, that they entertain probabilistic beliefs, objective or subjective, regarding any event in question. We argue that the formation of such beliefs calls for a deeper examination and for explicit modeling. Models of belief formation may enhance our understanding of the probabilistic beliefs when these exist, and may also help up characterize situations in which entertaining such beliefs is neither realistic nor necessarily rational.Decision making, Bayesian, Behavioral Economics
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Crowdsourcing markets have emerged as a popular platform for matching
available workers with tasks to complete. The payment for a particular task is
typically set by the task's requester, and may be adjusted based on the quality
of the completed work, for example, through the use of "bonus" payments. In
this paper, we study the requester's problem of dynamically adjusting
quality-contingent payments for tasks. We consider a multi-round version of the
well-known principal-agent model, whereby in each round a worker makes a
strategic choice of the effort level which is not directly observable by the
requester. In particular, our formulation significantly generalizes the
budget-free online task pricing problems studied in prior work.
We treat this problem as a multi-armed bandit problem, with each "arm"
representing a potential contract. To cope with the large (and in fact,
infinite) number of arms, we propose a new algorithm, AgnosticZooming, which
discretizes the contract space into a finite number of regions, effectively
treating each region as a single arm. This discretization is adaptively
refined, so that more promising regions of the contract space are eventually
discretized more finely. We analyze this algorithm, showing that it achieves
regret sublinear in the time horizon and substantially improves over
non-adaptive discretization (which is the only competing approach in the
literature).
Our results advance the state of art on several different topics: the theory
of crowdsourcing markets, principal-agent problems, multi-armed bandits, and
dynamic pricing.Comment: This is the full version of a paper in the ACM Conference on
Economics and Computation (ACM-EC), 201
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