An exploratory approach for adaptive policymaking by using multi-objective robust optimization

被引:81
作者
Hamarat, Caner [1 ]
Kwakkel, Jan H. [1 ]
Pruyt, Erik [1 ]
Loonen, Erwin T. [2 ]
机构
[1] Delft Univ Technol, NL-2600 AA Delft, Netherlands
[2] GEN Nederland, Utrecht, Netherlands
关键词
Adaptive policymaking; Deep uncertainty; Multi-objective robust optimization; Simulation optimization; Complex systems; Exploratory modeling and analysis; DECISION-MAKING; GENETIC ALGORITHM; SIMULATION OPTIMIZATION; DESIGN OPTIMIZATION; WATER-RESOURCES; MANAGEMENT; ADAPTATION; UNCERTAINTIES; COMPLEX;
D O I
10.1016/j.simpat.2014.02.008
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Developing robust policies for complex systems is a profound challenge because of their nonlinear and unpredictable nature. Dealing with these characteristics requires innovative approaches. A possible approach is to design policies that can be adapted over time in response to how the future unfolds. An essential part of adaptive policymaking is specifying under what conditions, and in which way, to adapt the policy. The performance of an adaptive policy is critically dependent on this: if the policy is adapted too late or too early, significant deterioration in policy performance can be incurred. An additional complicating factor is that in almost any policy problem, a multiplicity of divergent and potentially conflicting objectives has to be considered. In this paper we tackle both problems simultaneously through the use of multi-objective robust simulation optimization. Robust optimization helps in specifying appropriate conditions for adapting a policy, by identifying conditions that produce satisfactory results across a large ensemble of scenarios. Multi-objective optimization helps in identifying such conditions for a set of criteria, and providing insights into the tradeoffs between these criteria. Simulation is used for evaluating policy performance. This approach results in the identification of multiple alternative conditions under which to adapt a policy, rather than a single set of conditions. This creates the possibility of an informed policy debate on trade-offs. The approach is illustrated through a case study on designing a robust policy for supporting the transition toward renewable energy systems in the European Union. The results indicate that the proposed approach can be efficiently used for developing policy suggestions and for improving decision support for policymakers. By extension, it is possible to apply this methodology in dynamically complex and deeply uncertain systems such as public health, financial systems, transportation, and housing. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:25 / 39
页数:15
相关论文
共 107 条
[1]   Incorporating robustness into Genetic Algorithm search of stochastic simulation outputs [J].
Al-Aomar, R .
SIMULATION MODELLING PRACTICE AND THEORY, 2006, 14 (03) :201-223
[2]   Strategic (spatial) planning reexamined [J].
Albrechts, L .
ENVIRONMENT AND PLANNING B-PLANNING & DESIGN, 2004, 31 (05) :743-758
[3]   A simulated annealing technique for multi-objective simulation optimization [J].
Alrefaei, Mahmoud H. ;
Diabat, Ali H. .
APPLIED MATHEMATICS AND COMPUTATION, 2009, 215 (08) :3029-3035
[4]  
[Anonymous], HDB SIMULATION
[5]  
[Anonymous], 2012, INVESTMENT DECISION
[6]  
[Anonymous], EXPLORING CARBON PAT
[7]  
[Anonymous], 2009, INF DEC CHANG CLIM
[8]  
[Anonymous], MANAGE SCI
[9]  
[Anonymous], INNOVAT J PUBLIC SEC
[10]  
[Anonymous], 2001, WebPage