The Design of Optimal Policies with Layers: Implications for Catastrophe Reinsurance

Why have many transition economies succeeded by pursuing policies which are so different from the radical economic liberalization (shock therapy) that is normally credited for the economic success of countries of Central Europe? First, optimal policies are context dependent, they are specific for each stage of development and what worked in Slovenia cannot be expected to work in Mongolia. Second, even for the countries with the same level of development reforms that are necessary to stimulate growth are different; they depend on the previous history and on the path chosen. The reduction of government expenditure as a share of GDP did not undermine significantly the institutional capacity of the state in China, but in Russia and other CIS countries it turned out to be ruinous. The art of the policymaker is to create markets without causing government failure, as happened in many CIS countries.

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MANAGING INEQUALITY OVER BUSINESS CYCLES: OPTIMAL POLICIES WITH HETEROGENEOUS AGENTS AND AGGREGATE SHOCKS

International Economic Review ◽

10.1111/iere.12537 ◽

2021 ◽

Author(s):

François Le Grand ◽

Xavier Ragot

Keyword(s):

Business Cycles ◽

Heterogeneous Agents ◽

Aggregate Shocks ◽

Optimal Policies

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Optimal Policies for Quantum Markov Decision Processes

International Journal of Automation and Computing ◽

10.1007/s11633-021-1278-z ◽

2021 ◽

Author(s):

Ming-Sheng Ying ◽

Yuan Feng ◽

Sheng-Gang Ying

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Quantum Systems ◽

Sequential Decision Making ◽

Mathematical Framework ◽

Sequential Decision ◽

Learning Techniques ◽

Optimal Policies ◽

Markov Decision ◽

Programming Algorithms

AbstractMarkov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.

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