Probabilistic Choice and Optimal Contracts

Author(s):  
Suren Basov
2021 ◽  
Vol 31 (3) ◽  
pp. 1-22
Author(s):  
Gidon Ernst ◽  
Sean Sedwards ◽  
Zhenya Zhang ◽  
Ichiro Hasuo

We present and analyse an algorithm that quickly finds falsifying inputs for hybrid systems. Our method is based on a probabilistically directed tree search, whose distribution adapts to consider an increasingly fine-grained discretization of the input space. In experiments with standard benchmarks, our algorithm shows comparable or better performance to existing techniques, yet it does not build an explicit model of a system. Instead, at each decision point within a single trial, it makes an uninformed probabilistic choice between simple strategies to extend the input signal by means of exploration or exploitation. Key to our approach is the way input signal space is decomposed into levels, such that coarse segments are more probable than fine segments. We perform experiments to demonstrate how and why our approach works, finding that a fully randomized exploration strategy performs as well as our original algorithm that exploits robustness. We propose this strategy as a new baseline for falsification and conclude that more discriminative benchmarks are required.


2018 ◽  
Vol 173 ◽  
pp. 142-180 ◽  
Author(s):  
Jakša Cvitanić ◽  
Hao Xing

2005 ◽  
Vol 95 (5) ◽  
pp. 1369-1385 ◽  
Author(s):  
Sergei Guriev ◽  
Dmitriy Kvasov

The paper shows how time considerations, especially those concerning contract duration, affect incomplete contract theory. Time is not only a dimension along which the relationship unfolds, but also a continuous verifiable variable that can be included in contracts. We consider a bilateral trade setting where contracting, investment, trade, and renegotiation take place in continuous time. We show that efficient investment can be induced either through a sequence of constantly renegotiated fixed-term contracts; or through a renegotiation-proof “evergreen” contract—a perpetual contract that allows unilateral termination with advance notice. We provide a detailed analysis of properties of optimal contracts.


eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Fabian Grabenhorst ◽  
Ken-Ichiro Tsutsui ◽  
Shunsuke Kobayashi ◽  
Wolfram Schultz

Risk derives from the variation of rewards and governs economic decisions, yet how the brain calculates risk from the frequency of experienced events, rather than from explicit risk-descriptive cues, remains unclear. Here, we investigated whether neurons in dorsolateral prefrontal cortex process risk derived from reward experience. Monkeys performed in a probabilistic choice task in which the statistical variance of experienced rewards evolved continually. During these choices, prefrontal neurons signaled the reward-variance associated with specific objects (‘object risk’) or actions (‘action risk’). Crucially, risk was not derived from explicit, risk-descriptive cues but calculated internally from the variance of recently experienced rewards. Support-vector-machine decoding demonstrated accurate neuronal risk discrimination. Within trials, neuronal signals transitioned from experienced reward to risk (risk updating) and from risk to upcoming choice (choice computation). Thus, prefrontal neurons encode the statistical variance of recently experienced rewards, complying with formal decision variables of object risk and action risk.


2020 ◽  
Author(s):  
Giacomo Candian ◽  
Mikhail Dmitriev
Keyword(s):  

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