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Stable and extremely unequal

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 Added by Marc Henry
 Publication date 2021
  fields Economy
and research's language is English




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We highlight the tension between stability and equality in non transferable utility matching. We consider many to one matchings and refer to the two sides of the market as students and schools. The latter have aligned preferences, which in this context means that a schools utility is the sum of its students utilities. We show that the unique stable allocation displays extreme inequality between matched pairs.



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We study conditions for the existence of stable and group-strategy-proof mechanisms in a many-to-one matching model with contracts if students preferences are monotone in contract terms. We show that equivalence, properly defined, to a choice profile under which contracts are substitutes and the law of aggregate holds is a necessary and sufficient condition for the existence of a stable and group-strategy-proof mechanism. Our result can be interpreted as a (weak) embedding result for choice functions under which contracts are observable substitutes and the observable law of aggregate demand holds.
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180 - Chao Huang 2021
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We provide two characterizations, one axiomatic and the other neuro-computational, of the dependence of choice probabilities on deadlines, within the widely used softmax representation [ p_{t}left( a,Aright) =dfrac{e^{frac{uleft( aright) }{lambda left( tright) }+alpha left( aright) }}{sum_{bin A}e^{frac{uleft( bright) }{lambda left( tright) }+alpha left( bright) }}% ] where $p_{t}left( a,Aright) $ is the probability that alternative $a$ is selected from the set $A$ of feasible alternatives if $t$ is the time available to decide, $lambda$ is a time dependent noise parameter measuring the unit cost of information, $u$ is a time independent utility function, and $alpha$ is an alternative-specific bias that determines the initial choice probabilities reflecting prior information and memory anchoring. Our axiomatic analysis provides a behavioral foundation of softmax (also known as Multinomial Logit Model when $alpha$ is constant). Our neuro-computational derivation provides a biologically inspired algorithm that may explain the emergence of softmax in choice behavior. Jointly, the two approaches provide a thorough understanding of soft-maximization in terms of internal causes (neurophysiological mechanisms) and external effects (testable implications).
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