Secure collaborative supply chain planning and inverse optimization – The JELS model

It is a well-acknowledged fact that collaboration between different members of a supply chain yields a significant potential to increase overall supply chain performance. Sharing private information has been identified as prerequisite for collaboration and, at the same time, as one of its major obstacles. One potential avenue for overcoming this obstacle is Secure Multi-Party Computation (SMC). SMC is a cryptographic technique that enables the computation of any (well-defined) mathematical function by a number of parties without any party having to disclose its input to another party. In this paper, we show how SMC can be successfully employed to enable joint decision-making and benefit sharing in a simple supply chain setting. We develop secure protocols for implementing the well-known “Joint Economic Lot Size (JELS) Model” with benefit sharing in such a way that none of the parties involved has to disclose any private (cost and capacity) data. Thereupon, we show that although computation of the model’s outputs can be performed securely, the approach still faces practical limitations. These limitations are caused by the potential of “inverse optimization”, i.e., a party can infer another party’s private data from the output of a collaborative planning scheme even if the computation is performed in a secure fashion. We provide a detailed analysis of “inverse optimization” potentials and introduce the notion of “stochastic security”, a novel approach to assess the additional information a party may learn from joint computation and benefit sharing. Based on our definition of “stochastic security” we propose a stochastic benefit sharing rule, develop a secure protocol for this benefit sharing rule, and assess under which conditions stochastic benefit sharing can guarantee secure collaboration.     

An exploration into the influence of psychological profiles upon group decision making

This paper looks at a problem where a group, made up of individuals from a variety of different organisations but with similar technical backgrounds, had the task of producing a forecast for their particular industry. The forecast was to be published by their technical institute and was therefore of general interest in stimulating debate. The group members used a form of judgemental modelling to produce their individual forecasts. After these initial outcomes, the group was split into three sub-groups based upon a method of psychological profiling, with each subgroup repeating the exercise and producing a group-negotiated forecast. The results presented here show how group composition affects the way in which individuals negotiate towards a final outcome. The conclusions reached have implications for decision making aids using decision support systems, both for systems that aim to facilitate and for those that attempt to model the process. Although only a small experiment, the results suggest that there is tremendous potential benefit from this avenue of research when applied to the developing technologies of group decision and negotiation systems.     

服务关系对供应链信息分享和信任决策的影响研究

由于信息不对称,供应商和制造商之间的信息分享和信任决策是供应链协调的关键。为了获得充足供给,制造商分享其私有需求信息,但同时存在欺骗动机;鉴于制造商的信息分享,供应商进行信任决策和产能投资。本文通过构建服务价值模型,分析了供应商与制造商之间的服务关系对信息分享和信任决策的影响。研究结果表明如果供应商的服务价值足够高,制造商会选择真实分享信息,此时供应商可以充分信任制造商的信息;如果服务价值低,制造商会采取放大需求信息的欺骗行为,此时供应商应怀疑制造商所分享信息的真实性。     

EVALUATING THE PROSPECTS OF BENEFIT SHARING SCHEMES IN PROTECTING MOUNTAIN GORILLAS IN CENTRAL AFRICA

Presently, the mountain gorilla in Rwanda, Uganda, and the Democratic Republic of Congo is endangered mainly by poaching and habitat loss. This paper sets out to investigate the possible resolution of poaching involving the local community by using benefit sharing schemes with local communities. Using a bioeconomic model, the paper demonstrates that the current revenue sharing scheme yields suboptimal conservation outcomes. It is, however, shown that a performance‐linked benefit sharing scheme in which the Park Agency makes payment to the local community based on the growth of the gorilla stock can achieve socially optimal conservation. This scheme renders poaching effort by the local community, and therefore poaching fines and antipoaching enforcement toward the local community unnecessary. Given the huge financial outlay requirements for the ideal benefit sharing scheme, the Park Agencies in central Africa could reap more financial benefits for use in conservation if they employ an oligopolistic pricing strategy for gorilla tourism.     

Reward systems for intra-organizational knowledge sharing

Knowledge sharing is one of the most critical steps in knowledge management activities. To achieve effective knowledge sharing, it is important to encourage workers to share their knowledge for the best interests of the firm. However, successfully exerting this encouragement is very challenging. In this paper, we develop a formal model and analyze reward systems for intra-organizational knowledge sharing. Specifically, two common forms of reward systems are considered; individual-based reward which is based on the individual contribution of valuable knowledge, and group-based reward which is based on the contribution of the whole group through knowledge sharing to the firm performance. Through the analysis, we derive a simple optimal individual-based reward system which depends on the amount and the productivity of shared knowledge. The system balances the benefit from knowledge sharing of each worker with the costs related with it. Next, it is found that group-based reward is not only less efficient than individual-based reward, but it also subject to a potential productivity problem, in which workers with more productive knowledge do not participate in knowledge sharing. Finally, it is shown that several organizational factors can complement reward systems in increasing the performance of KM and can mitigate the productivity problem. Insights from our analysis could help managers to understand important considerations in rewarding knowledge sharing, and could provide them with guides to implement reward systems.     

Optimal exercise of jointly held real options: A Nash bargaining approach with value diversion

This paper provides a two-stage decision framework in which two or more parties exercise a jointly held real option. We show that a single party’s timing decision is always socially efficient if it precedes bargaining on the terms of sharing. However, if the sharing rule is agreed before the exercise timing decision is made, then socially optimal timing is attained only if there is a cash payment element in the division of surplus. If the party that chooses the exercise timing can divert value from the project, then the first-best outcome may not be possible at all and the second-best outcome may be implemented using a contract that is generally not optimal in the former cases. Our framework contributes to the understanding of a range of empirical regularities in corporate and entrepreneurial finance.     

基于社会网络分析的虚拟社区知识共享研究进展可视化分析

虚拟社区知识共享是近年知识共享研究领域的热点议题之一。本研究以2004~2014年间CNKI收录的188篇虚拟社区知识共享相关文献为研究对象,采用社会网络分析技术,运用Excel、Bibexcel和Ucinet软件,分别对文献共被引和作者共被引进行了可视化分析,解析了该研究领域的网络结构、重要文献、权威作者、主题分布和学术团体,并在此基础上对当前国内虚拟社区知识共享的研究现状进行了总结、对未来的研究方向进行了展望。     

不同补贴对象下闭环供应链信息共享价值研究

在一个制造商和一个零售商组成的二级闭环供应链中,为研究政府补贴下供应链需求信息共享对决策的影响及共享价值,针对两种补贴对象,构建并求解无信息共享和信息共享博弈模型,并进行仿真验证.研究表明:1)两种补贴对象下,制造商均能从信息共享中获益,零售商仅在绿色成本和回收成本较低时才会从信息共享中获益;绿色成本和回收成本稍高时,设计基于谈判势力的信息补偿机制能有效促进零售商共享信息.2)产品绿色度和回收率随预测需求量的提高而提高,批发价和零售价仅在回收成本较低时,才会随预测需求量的提高而提高.3)在仅补贴一方情况下,当补贴对象为低补贴一方时,两个主体所获的信息共享价值大;若对两者的补贴均较低,两个主体均希望补贴对象为零售商;若对两者的补贴均较高,零售商不愿共享需求信息.     

考虑风险的产业技术联盟知识共享演化博弈研究

知识共享是促进产业技术联盟绩效增长的重要途径。考虑产业技术联盟成员风险偏好的差异性,引入成员风险因子与知识共享收益因素,构建基于风险因子和知识共享收益非线性关系的演化博弈支付矩阵,并求得博弈的均衡解。通过TD产业联盟的案例分析,仿真模拟风险因子、收益分配系数等因素对知识共享意愿的影响。研究结果表明:联盟成员的共享意愿对收益分配系数的变化非常敏感;风险因子和知识共享程度对联盟成员的共享意愿影响较大。     

知识共享与间接互惠:基于计算实验方法的研究

团队中,个体知识共享行为的回报往往来自第三方而非受助者,而以直接互惠为视角的知识共享研究却不足以解释此类现象。采用基于计算机仿真实验方法,通过赋予Agent记忆、推理、决策和沟通等能力,研究受表型背叛、组织信任氛围及声誉传播机制影响下,间接互惠机制对团队大范围知识共享行为的维系机理。研究发现,由于辨别者的存在,即使无条件共享者会成为绝对多数,隐藏者并不会大量侵入群体。这意味着间接互惠机制维系了团队大范围知识共享行为。在低的组织信任氛围水平下,辨别者和隐藏者虽然能够共存,团队内的知识共享行为却没有出现。即使只有少数成员能观察到周围同事的知识共享行为,只要声誉信息能够有效传播,间接互惠对广泛知识共享行为的维系作用就能够发挥。     

Maximizing benefits from crowdsourced data

Crowds of people can solve some problems faster than individuals or small groups. A crowd can also rapidly generate data about circumstances affecting the crowd itself. This crowdsourced data can be leveraged to benefit the crowd by providing information or solutions faster than traditional means. However, the crowdsourced data can hardly be used directly to yield usable information. Intelligently analyzing and processing crowdsourced information can help prepare data to maximize the usable information, thus returning the benefit to the crowd. This article highlights challenges and investigates opportunities associated with mining crowdsourced data to yield useful information, as well as details how crowdsource information and technologies can be used for response-coordination when needed, and finally suggests related areas for future research.     

批发价格与收益共享合约并存下的供应链信息泄露研究

信息泄露是供应链企业之间进行信息共享的障碍之一,本文考虑供应商与在位者零售商、进入者零售商之间缔结不同类型合约下的信息泄露问题。鉴于供应商与在位者之间具有较长期的合作关系,他们之间以收益共享合约进行交易,而考虑到进入者零售商新进入此市场,他与供应商之间以批发价格合约进行交易。基于信号博弈的研究框架,分析了此设定下所导致的纯策略完美贝叶斯均衡,并给出了分离均衡、混同均衡以及非泄露均衡出现的条件。研究发现,当供应商与在位者之间的收益共享比例和供应商向进入者提供的批发价格满足一定条件时,供应商有动机不泄露在位者的订货量信息,即出现非泄露均衡。最后,通过数值算例对在位者、进入者以及供应商在分离均衡和非泄露均衡中的利润进行了比较分析。     

延迟效应影响下的产学研协同创新知识共享研究

在高校和企业组成的产学研协同创新知识共享体系中,考虑成员的知识共享策略均存在延迟效应时的影响,构建了基于双延迟效应的动态微分模型。运用极大值原理,得到了协同创新主体在集中和分散两种决策下的最优知识共享策略、最优收益和体系的知识水平。研究发现:延迟时间的存在对高校和企业的知识共享策略具有负面效应,且均低于发生即时效应的情形;集中式决策并不总是优于分散式决策,两个延迟时间在满足一定的条件时,选择集中式决策为最优,否则双方企业将追求自身利益最大化而选择分散式决策;分散决策下,利润分配比例达到一定条件时,企业才愿意为高校提供补贴。且高校的知识共享延迟时间增加时,企业将提高该补贴比例。补贴比例越大,高校的知识共享水平越高,但对企业的知识共享策略没有影响。     

考虑平台影响因素的虚拟社区知识共享演化博弈研究

知识共享是虚拟社区用户学习和传播知识的重要途径。本文考虑到虚拟社区平台对参与用户知识共享行为的激励作用,引入平台的奖励和不分享知识所产生的机会成本,以及知识共享收益因素,构建考虑虚拟社区平台影响因素的虚拟社区知识共享演化博弈支付矩阵,并求得演化均衡解。通过小米社区的案例引入,仿真模拟平台对用户知识共享的奖励、机会成本、收益分配系数以及参与用户共享程度等因素对参与用户知识共享行为意愿的影响。研究发现:虚拟社区参与用户的知识共享意愿对因为知识共享所产生新知识的收益系数变化较为敏感;平台的奖励以及不分享知识所产生的机会成本对用户知识共享意愿影响巨大;同时,不同参与用户因为知识水平层次不同,参与知识共享的目的也不相同,这会影响到他们的知识共享策略选择。     

奖惩机制下零售商信息分享对闭环供应链的影响研究

研究奖惩机制下零售商的信息分享策略以及对闭环供应链的影响,建立了由制造商和零售商以及消费者组成的闭环供应链,其中制造商负责回收废旧产品并进行再制造。分别研究了集中式决策的情形和分散式决策下零售商信息分享和不分享的情形。研究发现,若政府在社会总福利目标中不考虑奖惩成本且回收难度较小时,零售商信息分享使社会总福利提高,反之,导致社会总福利降低;零售商信息分享总会使消费者剩余的期望值降低,但能够提高废旧产品的回收率。最后,针对零售商信息分享引起社会福利提高的情况设计了回收责任分担契约激励零售商分享信息。关键词:信息分享;奖惩机制;闭环供应链;社会福利;消费者剩余     

在线知识社区中基于个体异质性的知识与社会系统协同演化研究

在Web 2.0环境下,在线知识社区(Online Knowledge Community, OKC)已成为人们进行学习、利用、分享、传播知识的重要平台。然而,现有关于OKC的研究较多关注其中社会系统对个体知识建构、群体知识建构的影响,缺少从OKC演化的视角,以用户为基础对OKC中社会系统与知识系统间双向交互影响规律的研究。为此,本文将个体异质性作为影响因素引入到OKC的研究中,在一个开放的在线知识社区中模拟个体的观点、认知结构以及个体间形成的社会关系与知识系统的共同演化。将个体间以信任为基础形成的关系网络视为OKC中的社会系统,将群体依据自身认知结构表达出的全部观点视为社区中的知识系统,进而采取了社会科学计算实验的研究方法构建了一个OKC中社会系统与知识系统协同演化模型,并在模型中引入了个体的内在和外在异质性作为影响因素。研究结果表明OKC中个体的学习能力、容忍度、活跃度等个体因素,对其中知识观点的传播与社区中社会网络的演化影响显著。本研究希望能够为OKC中知识的自组织、序化的研究提供理论支持。     

需求信息不对称下供应链中流程与产品创新模式选择研究

在制造商进行流程创新和产品创新的供应链中,当需求预测信息不对称时,研究了零售商的需求信息分享策略以及制造商最优创新模式。利用精炼贝叶斯理论和Stackelberg博弈模型,首先求得了每一种创新模式下信息分享价值、促进需求信息分享的激励机制以及供应链均衡的信息分享策略,其次对两种创新模式下利润进行对比分析。研究发现,在两种创新供应链模型中,当创新效率较高和较低时,零售商自愿分享和不分享需求信息分别是均衡策略;当创新效率处于中间水平时,通过设计一个讨价还价机制可以使零售商分享需求信息成为一个均衡。此外,当消费者对质量(价格)更敏感时,制造商选择产品创新(流程创新)模式可以使供应链成员都获益。     

On the value of information sharing and cooperative price setting

We consider a setting of two firms that sell substitutable products under price competition. We show that private signals enable firms to improve market forecast and earn higher profits. Provided that their private signals are not perfectly correlated, firms can benefit from sharing signals with each other. This is irrespective of product substitutability. Moreover, information sharing is a strategic complement to cooperative price setting to improve the profit performance of firms.     

Sequencing interval situations and related games

Uncertainty accompanies almost every situation in real world and it influences our decisions. In sequencing situations it may affect parameters used to determine an optimal order in the queue, and consequently the decision of whether (or not) to rearrange the queue by sharing the realized cost savings. This paper extends the analysis of one-machine sequencing situations and their related cooperative games to a setting with interval data, i.e. when the agents’ costs per unit of time and/or processing time in the system lie in intervals of real numbers obtained by forecasting their values. The question addressed here is: How to determine an optimal order (if the initial order in the queue is not so) and which approach should be used to motivate the agents to adopt the optimal order? This question is an important one that deserves attention both in theory and practice.     

Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners

Pathology ordering by general practitioners (GPs) is a significant contributor to rising health care costs both in Australia and worldwide. A thorough understanding of the nature and patterns of pathology utilization is an essential requirement for effective decision support for pathology ordering. In this paper a novel methodology for integrating data mining and case-based reasoning for decision support for pathology ordering is proposed. It is demonstrated how this methodology can facilitate intelligent decision support that is both patient-oriented and deeply rooted in practical peer-group evidence. Comprehensive data collected by professional pathology companies provide a system-wide profile of patient-specific pathology requests by various GPs as opposed to that limited to an individual GP practice. Using the real data provided by XYZ Pathology Company in Australia that contain more than 1.5 million records of pathology requests by general practitioners (GPs), we illustrate how knowledge extracted from these data through data mining with Kohonen’s self-organizing maps constitutes the base that, with further assistance of modern data visualization tools and on-line processing interfaces, can provide “peer-group consensus” evidence support for solving new cases of pathology test ordering problem. The conclusion is that the formal methodology that integrates case-based reasoning principles which are inherently close to GPs’ daily practice, and data-driven computationally intensive knowledge discovery mechanisms which can be applied to massive amounts of the pathology requests data routinely available at professional pathology companies, can facilitate more informed evidential decision making by doctors in the area of pathology ordering.