The core and shapley value analysis for cooperative formation of procurement networks

Formation of high value procurement networks involves a bottom-up assembly of complex production, assembly, and exchange relationships through supplier selection and contracting decisions, where suppliers are intelligent and rational agents who act strategically. In this paper we address the problem of forming procurement networks for items with value adding stages that are linearly arranged We model the problem of Procurement Network Formation (PNF) for multiple units of a single item as a cooperative game where agents cooperate to form a surplus maximizing procurement network and then share the surplus in a stable and fair manner We first investigate the stability of such networks by examining the conditions under which the core of the game is non-empty. We then present a protocol, based on the extensive form game realization of the core, for forming such networks so that the resulting network is stable. We also mention a key result when the Shapley value is applied as a solution concept.

A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks

Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) lambda-coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The lambda-coverage problem is concerned with finding a set of k key nodes having minimal size that can influence a given percentage lambda of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the lambda-coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient. Note to Practitioners-In recent times, social networks have received a high level of attention due to their proven ability in improving the performance of web search, recommendations in collaborative filtering systems, spreading a technology in the market using viral marketing techniques, etc. It is well known that the interpersonal relationships (or ties or links) between individuals cause change or improvement in the social system because the decisions made by individuals are influenced heavily by the behavior of their neighbors. An interesting and key problem in social networks is to discover the most influential nodes in the social network which can influence other nodes in the social network in a strong and deep way. This problem is called the target set selection problem and has two variants: 1) the top-k nodes problem, where we are required to identify a set of k influential nodes that maximize the number of nodes being influenced in the network and 2) the lambda-coverage problem which involves finding a set of influential nodes having minimum size that can influence a given percentage lambda of the nodes in the entire network. There are many existing algorithms in the literature for solving these problems. In this paper, we propose a new algorithm which is based on a novel interpretation of information diffusion in a social network as a cooperative game. Using this analogy, we develop an algorithm based on the Shapley value of the underlying cooperative game. The proposed algorithm outperforms the existing algorithms in terms of generality or computational complexity or both. Our results are validated through extensive experimentation on both synthetically generated and real-world data sets.

Determining Top K Nodes in Social Networks using the Shapley Value

In this paper, we consider the problem of selecting, for any given positive integer k, the top-k nodes in a social network, based on a certain measure appropriate for the social network. This problem is relevant in many settings such as analysis of co-authorship networks, diffusion of information, viral marketing, etc. However, in most situations, this problem turns out to be NP-hard. The existing approaches for solving this problem are based on approximation algorithms and assume that the objective function is sub-modular. In this paper, we propose a novel and intuitive algorithm based on the Shapley value, for efficiently computing an approximate solution to this problem. Our proposed algorithm does not use the sub-modularity of the underlying objective function and hence it is a general approach. We demonstrate the efficacy of the algorithm using a co-authorship data set from e-print arXiv (www.arxiv.org), having 8361 authors.

A Shapley Value Analysis to Coordinate the Formation of Procurement Networks

In this paper we address the problem of forming procurement networks for items with value adding stages that are linearly arranged. Formation of such procurement networks involves a bottom-up assembly of complex production, assembly, and exchange relationships through supplier selection and contracting decisions. Research in supply chain management has emphasized that such decisions need to take into account the fact that suppliers and buyers are intelligent and rational agents who act strategically. In this paper, we view the problem of procurement network formation (PNF) for multiple units of a single item as a cooperative game where agents cooperate to form a surplus maximizing procurement network and then share the surplus in a fair manner. We study the implications of using the Shapley value as a solution concept for forming such procurement networks. We also present a protocol, based on the extensive form game realization of the Shapley value, for forming these networks.

Influence limitation in multi-campaign social networks: A Shapley value based approach

We investigate the problem of influence limitation in the presence of competing campaigns in a social network. Given a negative campaign which starts propagating from a specified source and a positive/counter campaign that is initiated, after a certain time delay, to limit the the influence or spread of misinformation by the negative campaign, we are interested in finding the top k influential nodes at which the positive campaign may be triggered. This problem has numerous applications in situations such as limiting the propagation of rumor, arresting the spread of virus through inoculation, initiating a counter-campaign against malicious propaganda, etc. The influence function for the generic influence limitation problem is non-submodular. Restricted versions of the influence limitation problem, reported in the literature, assume submodularity of the influence function and do not capture the problem in a realistic setting. In this paper, we propose a novel computational approach for the influence limitation problem based on Shapley value, a solution concept in cooperative game theory. Our approach works equally effectively for both submodular and non-submodular influence functions. Experiments on standard real world social network datasets reveal that the proposed approach outperforms existing heuristics in the literature. As a non-trivial extension, we also address the problem of influence limitation in the presence of multiple competing campaigns.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.

An analysis of embeddedness in the value creation in interorganisational networks: an illustrative example in Brazil

The purpose of this article is to propose a methodological approach to analyse how the positioning of actors in an interorganisational network can influence the elements of value creation. This study fills a gap in the existing literature by exploring the relationship between the positioning of actors and the value creation in a context of interorganisational networks. The case study method was employed and the data was obtained from four companies of an interorganisational network, located in Brazil, which produces earthmoving equipments. The central actor in this network is benefited through access to resources, power and information of the other network actors. The centrality position seems to help this company in the absorption and diffusion of knowledge among the other network actors. The research indicates, that a dense core (through strong ties) and redundancy (for triangulation and knowledge absorption), benefited the following value creation elements: tangible; intangible; services and economic.

Investigating the value of privacy on online social networks: conjoint analysis

Popularity of Online Social Networks has been recently overshadowed by the privacy problems they pose. Users are getting increasingly vigilant concerning information they disclose and are strongly opposing the use of their information for commercial purposes. Nevertheless, as long as the network is offered to users for free, providers have little choice but to generate revenue through personalized advertising to remain financially viable. Our study empirically investigates the ways out of this deadlock. Using conjoint analysis we find that privacy is indeed important for users. We identify three groups of users with different utility patterns: Unconcerned Socializers, Control-conscious Socializers and Privacy-concerned. Our results provide relevant insights into how network providers can capitalize on different user preferences by specifically addressing the needs of distinct groups in the form of various premium accounts. Overall, our study is the first attempt to assess the value of privacy in monetary terms in this context.

How are global value chains fragmented and extended in China's domestic production networks?

Global value chains are supported not only directly by domestic regions that export goods and services to the world market, but also indirectly by other domestic regions that provide parts, components, and intermediate services to final exporting regions. In order to better understand the nature of a country’s position and degree of participation in global value chains, we need to more fully examine the role of individual domestic regions. Understanding the domestic components of global supply chains is especially important for large developing countries like China and India, where there may be large variations in economic scale and development between domestic regions. This paper proposes a new framework for measuring domestic linkages to global value chains. This framework measures domestic linkages by endogenously embedding a country’s domestic interregional input-output (IO) table in an international IO model. Using this framework, we can more clearly describe how global production is fragmented and extended through linkages across a country’s domestic regions. This framework will also enable us to estimate how value added is created and distributed in both domestic and international segments of global value chains. For examining the validity and usefulness of this new approach, some numerical results are presented and discussed based on the 2007 Chinese interregional IO table, China customs statistics at the provincial level, and World Input-Output Tables (WIOTs).

NEMAWASHI: Attaining Value Stream alignment within Complex Organizational Networks

In the process of value creation, organizations perform an intense intra-organizational dialog through which internal VS alignment is achieved towards certain strategic objectives. Within the context of complex organizational networks, were goal conflicts are preprogrammed through incentive structures, VS alignment as legitimation of action towards strategic goals has special interest. On the one hand it facilitates the access to necessary resources for goal achievement and on the other it increases the sustainability and supports commonly agreed upon decisions leading to success. This paper provides a winnerless process (WLP) differential equations model for quantifying intra-organizational value stream (VS) alignment dynamics that can help design sustainable lean management solutions. This paper presents ongoing research results that show how the model was implemented in one industrial facility.

Inter-organisational networks, value creation and the process of technology integration in research and development

Traditional methods of R&D management are no longer sufficient for embracing innovations and leveraging complex new technologies to fully integrated positions in established systems. This paper presents the view that the technology integration process is a result of fundamental interactions embedded in inter-organisational activities. Emerging industries, high technology companies and knowledge intensive organisations owe a large part of their viability to complex networks of inter-organisational interactions and relationships. R&D organisations are the gatekeepers in the technology integration process with their initial sanction and motivation to develop technologies providing the first point of entry. Networks rely on the activities of stakeholders to provide the foundations of collaborative R&D activities, business-to-business marketing and strategic alliances. Such complex inter-organisational interactions and relationships influence value creation and organisational goals as stakeholders seek to gain investment opportunities. A theoretical model is developed here that contributes to our understanding of technology integration (adoption) as a dynamic process, which is simultaneously structured and enacted through the activities of stakeholders and organisations in complex inter-organisational networks of sanction and integration.

Creating and managing value in collaborative networks

This is a theoretical paper that examines the interplay between individual and collective capabilities and competencies and value transactions in collaborative environments. The theory behind value creation is examined and two types of value are identified, internal value (Shareholder value) and external value (Value proposition). The literature on collaborative enterprises/network is also examined with particular emphasis on supply chains, extended/virtual enterprises and clusters as representatives of different forms and maturities of collaboration. The interplay of value transactions and competencies and capabilities are examined and discussed in detail. Finally, a model is presented which consists of value transactions and a table which compares the characteristics of different types of collaborative enterprises/networks. It is proposed that this model presents a platform for further research to develop an in-depth understanding into how value may be created and managed in collaborative enterprises/networks.