基于遗传算法的创新竞赛机制优化
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摘要
近年来,越来越多外部和内部影响作用于新产品研发过程中,导致产品设计的复杂性的需求也随之大大增加。因此传统的产品内部研发机制已经不足以应对市场激烈的竞争。为了迎合市场竞争需求,企业由内部研发向开放式研发转型,通过外部力量解决产品设计创新问题。此外,基于互联网的计算机辅助创新(CAI)进一步提高了开放式创新的高效性。再加之创新竞赛被视为最有效的征集创新方案的平台,因此为解决上述问题,基于互联网的创新竞争是最理想化的有力途径。这一创新竞争平台能够有效地被用于包括学术,商业等广泛领域。
在创新竞赛中,主要参与方有发起人和参与者。发起人的目标是通过外部资源(参与者)来解决与创新相关的问题。发起人设定奖赏机制激励参与者积极投入,从而获得最满意方案。提供最满意方案的参与者获得奖励。创新竞赛中的参与者一般数量较大,参与者最终所得的回报与投入不一定成比例,这一现象会导致参与者参赛热情与投入时间降低,进而影响竞赛的效果。有学者提出,为了消除这种努力不足现象,参赛者应该减少到两名。之后有学者证明了保留参赛者人数但改变创新竞赛奖励制度可以有效提高竞赛效果,原因有二：大量的参与者能够增加参赛方案的多样性；奖励制度由之前的固定奖额变为提成制度能够有效地提高参赛者的积极性。除此以外,就增加竞赛效率的问题,一些学者提出增加竞赛轮数的方案。合格的参与者进入下一轮比赛,随着比赛的推进比赛人数随之降低,进而每个留下来的参与者获胜的概率增加,因此他们会投入更多时间和精力来赢得比赛。但由此会产生增加竞赛轮数会导致竞赛成本增加的问题。本片文章旨在通过优化创新竞赛机制解决上述问题,进而提高发起人收益。
本文的研究方法灵感来源于达尔文的“优胜劣汰”进化论。因而,本论文构建了基于遗传算法的数学模型以仿真模拟创新竞赛的三大阶段：学习,变化和选择。本文采用随机交叉组合模拟学习与变化过程。而在选择过程中引入适度函数,并根据适度值计算被选概率,从而对方案进行选择。模拟过程中发现了信息公开化产生的学习行为能够对可行方案产生速度正向促进。仿真模拟后得出的结论有三：1)参赛者之间的学习行为能够有效缩短可行方案产生的时间。同时本文利用随机概率模型进一步的验证了该结论；2)一旦可行方案出现以后,整个竞赛整体方案水平急剧上升；3)多奖励比单奖励方案更能加速可行方案的产生,在此种机制下,信息公开与否对可行方案产生的速度没有影响。随后本文对模型中主要参数进行了敏感性分析,总结出不同因素对竞赛效果的影响。本文利用算例进一步阐释了结论在实际创新竞赛机制设计中的应用。在文章最后进行了总结,提出本研究方法的局限性,并对未来研究方向提出了建议。
In recent years, there has been rapidly increasing activities conducted on integrating internal and external input into the development of new products. Along with the increase of competitiveness the complexity of product design systems is correspondingly increased. Thus the traditional internal R&D innovation can no longer satisfy the intensive competition in the market place. To cope with the increasingly competitiveness, the in-house R&D strategy shifts to an open R&D strategy by which the innovation processes that are carried out by the external world. Additionally, the emergence of the internet has facilitated the development of open innovation through Computer Aided Innovation (CAI). Under this circumstance, the fact that activities of innovation process are conducted through internet instead of in the physical world is considered to be more efficient. Therefore, IT-based innovation contests are the very approach to solve the challenge. This IT-based innovation contests can further benefit various areas, including academic, business and so on.
In an innovation contest, an organization (the seeker) aims to solve an innovation-related problem with external help which refers to a number of individuals (the solvers). Seeker sets up reward system to motivate solvers with the purpose of getting the most satisfied solution from a diversified solution pool. In turn, the solver who generates the most valuable solution gets the reward by devoting to the game. In this paper, the focus is on how to make an innovation contest work more efficiently (maximizing seeker's profit). Facing a large population of contestants, solvers have the concern that their effort might not be financially rewarded. Thus they tend to be less productive which leads the innovation contest to be less effective. Although scholars mentioned that in order to eliminate this underinvestment affect solvers should be reduced to two, a latest literature argues that "the benefits of diversity can outweigh or at least mitigate the negative effect of underinvestment" insisted by researchers. Furthermore, they also mention that changing award system, from existing fix-price award system to performance-contingent award system, can reduce the inefficiency of innovation contests as well. Besides changing award system, some scholars have discovered that increasing innovation contests round can significantly motivate solvers. Only qualified solvers can participate in the following rounds. In the later rounds, each solver makes more effort in the contest, because he/she has higher probability to win. However, new challenge has risen that is running more rounds massively shrinks seeker's profit. This thesis aims to solve this new challenge by optimizing mechanism of innovation contests and then improve seeker's profit.
As far as discussed here, this paper proposes an approach, which is inspired by survival of the fittest concept of Darwin, to solve this problem. Based on genetic algorithm theory, a computational model is constructed to simulate processes of innovation contests which are learning, variation, and selection. This paper interprets learning and variation processes as random crossover and recombination. For selection process, fitness function is constructed. Based on fitness value of each solution, probability of being selected is assigned and then selection process is performed. This article uncovers a possible affection of information sharing and learning behavior on the discovery of the first viable solution in innovation contests. By conducting simulation, this paper demonstrates three main contributive findings: First, learning behavior of solvers in innovation contests can accelerate the discovery the first viable solution. This finding is proved by conducting a computational simulation and further confirmed by comparing with a stochastic model; second, once the viable solution comes out, it spreads rapidly to the rest of solvers. Then, the entire solution level increases correspondingly; third, multi-reward strategy works even better on accelerating the discovery of the viable solution. Under this condition, learning behavior no longer guides the environmental selection. Later on, sensitivity analysis is carried out which figures out the influences of main elements on the innovation contest. Later, this paper constructs a numerical example which elaborates the application of the results in designing real-world innovation contest. The last part of this paper summarizes all the results and applications, and figures out the limitation of our research and provides the outlooks for further research.
在创新竞赛中,主要参与方有发起人和参与者。发起人的目标是通过外部资源(参与者)来解决与创新相关的问题。发起人设定奖赏机制激励参与者积极投入,从而获得最满意方案。提供最满意方案的参与者获得奖励。创新竞赛中的参与者一般数量较大,参与者最终所得的回报与投入不一定成比例,这一现象会导致参与者参赛热情与投入时间降低,进而影响竞赛的效果。有学者提出,为了消除这种努力不足现象,参赛者应该减少到两名。之后有学者证明了保留参赛者人数但改变创新竞赛奖励制度可以有效提高竞赛效果,原因有二：大量的参与者能够增加参赛方案的多样性；奖励制度由之前的固定奖额变为提成制度能够有效地提高参赛者的积极性。除此以外,就增加竞赛效率的问题,一些学者提出增加竞赛轮数的方案。合格的参与者进入下一轮比赛,随着比赛的推进比赛人数随之降低,进而每个留下来的参与者获胜的概率增加,因此他们会投入更多时间和精力来赢得比赛。但由此会产生增加竞赛轮数会导致竞赛成本增加的问题。本片文章旨在通过优化创新竞赛机制解决上述问题,进而提高发起人收益。
本文的研究方法灵感来源于达尔文的“优胜劣汰”进化论。因而,本论文构建了基于遗传算法的数学模型以仿真模拟创新竞赛的三大阶段：学习,变化和选择。本文采用随机交叉组合模拟学习与变化过程。而在选择过程中引入适度函数,并根据适度值计算被选概率,从而对方案进行选择。模拟过程中发现了信息公开化产生的学习行为能够对可行方案产生速度正向促进。仿真模拟后得出的结论有三：1)参赛者之间的学习行为能够有效缩短可行方案产生的时间。同时本文利用随机概率模型进一步的验证了该结论；2)一旦可行方案出现以后,整个竞赛整体方案水平急剧上升；3)多奖励比单奖励方案更能加速可行方案的产生,在此种机制下,信息公开与否对可行方案产生的速度没有影响。随后本文对模型中主要参数进行了敏感性分析,总结出不同因素对竞赛效果的影响。本文利用算例进一步阐释了结论在实际创新竞赛机制设计中的应用。在文章最后进行了总结,提出本研究方法的局限性,并对未来研究方向提出了建议。
In recent years, there has been rapidly increasing activities conducted on integrating internal and external input into the development of new products. Along with the increase of competitiveness the complexity of product design systems is correspondingly increased. Thus the traditional internal R&D innovation can no longer satisfy the intensive competition in the market place. To cope with the increasingly competitiveness, the in-house R&D strategy shifts to an open R&D strategy by which the innovation processes that are carried out by the external world. Additionally, the emergence of the internet has facilitated the development of open innovation through Computer Aided Innovation (CAI). Under this circumstance, the fact that activities of innovation process are conducted through internet instead of in the physical world is considered to be more efficient. Therefore, IT-based innovation contests are the very approach to solve the challenge. This IT-based innovation contests can further benefit various areas, including academic, business and so on.
In an innovation contest, an organization (the seeker) aims to solve an innovation-related problem with external help which refers to a number of individuals (the solvers). Seeker sets up reward system to motivate solvers with the purpose of getting the most satisfied solution from a diversified solution pool. In turn, the solver who generates the most valuable solution gets the reward by devoting to the game. In this paper, the focus is on how to make an innovation contest work more efficiently (maximizing seeker's profit). Facing a large population of contestants, solvers have the concern that their effort might not be financially rewarded. Thus they tend to be less productive which leads the innovation contest to be less effective. Although scholars mentioned that in order to eliminate this underinvestment affect solvers should be reduced to two, a latest literature argues that "the benefits of diversity can outweigh or at least mitigate the negative effect of underinvestment" insisted by researchers. Furthermore, they also mention that changing award system, from existing fix-price award system to performance-contingent award system, can reduce the inefficiency of innovation contests as well. Besides changing award system, some scholars have discovered that increasing innovation contests round can significantly motivate solvers. Only qualified solvers can participate in the following rounds. In the later rounds, each solver makes more effort in the contest, because he/she has higher probability to win. However, new challenge has risen that is running more rounds massively shrinks seeker's profit. This thesis aims to solve this new challenge by optimizing mechanism of innovation contests and then improve seeker's profit.
As far as discussed here, this paper proposes an approach, which is inspired by survival of the fittest concept of Darwin, to solve this problem. Based on genetic algorithm theory, a computational model is constructed to simulate processes of innovation contests which are learning, variation, and selection. This paper interprets learning and variation processes as random crossover and recombination. For selection process, fitness function is constructed. Based on fitness value of each solution, probability of being selected is assigned and then selection process is performed. This article uncovers a possible affection of information sharing and learning behavior on the discovery of the first viable solution in innovation contests. By conducting simulation, this paper demonstrates three main contributive findings: First, learning behavior of solvers in innovation contests can accelerate the discovery the first viable solution. This finding is proved by conducting a computational simulation and further confirmed by comparing with a stochastic model; second, once the viable solution comes out, it spreads rapidly to the rest of solvers. Then, the entire solution level increases correspondingly; third, multi-reward strategy works even better on accelerating the discovery of the viable solution. Under this condition, learning behavior no longer guides the environmental selection. Later on, sensitivity analysis is carried out which figures out the influences of main elements on the innovation contest. Later, this paper constructs a numerical example which elaborates the application of the results in designing real-world innovation contest. The last part of this paper summarizes all the results and applications, and figures out the limitation of our research and provides the outlooks for further research.
引文
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