Implementing Environmental and Social Responsibility Programs in Supply Networks Through Multiunit Bilateral Negotiation

Involving suppliers deep in the supply chain is critical for the success of environmental and social responsibility (ESR) initiatives. Administering ESR programs throughout a complex supply network, however, is challenging. In this paper, we apply a multiunit bilateral bargaining framework to coordinate ESR investments in a general supply network and analyze to what extent an ESR initiator should directly engage the higher-tier suppliers as opposed to delegating that responsibility to the first-tier suppliers. Our bargaining framework not only generalizes the conventional Shapley value approach by allowing the flexibility of modeling imbalanced power distribution among the firms but also provides an explicit way of implementing the resulting gain sharing among the firms through negotiated contract terms. We show that the eventual structure of ESR negotiation relationships can be derived by finding a shortest path tree in the supply network with the arc cost defined as the logarithm of the negotiating parties’ relative bargaining power. These developments allow us to analyze ESR implementation in generally extended supply networks. We find that the ESR initiator tends to delegate ESR negotiations to a supplier that is strong in negotiations with higher-tier suppliers. When the supply network is complex (i.e., wide and deep), directly engaging all suppliers can lead to a larger gain by the initiator than fully delegating the negotiations with higher-tier suppliers to the first-tier ones. However, as the network gets increasingly complex, the ESR initiator tends to directly engage a reduced percentage of higher-tier suppliers. We further extend our analysis to situations where the ESR relationships are sequentially formed in a decentralized manner, where the benefit of ESR depends on the collective choice of the firms’ investment levels, where multiple ESR programs are implemented in the network, and where ESR investments depend on the negotiation relationships. This paper was accepted by David Simchi-Levi, operations management.

A Hybrid Concession Mechanism for Negotiating Software Agents in Competitive Environments

This paper presents a new hybrid concession mechanism for negotiating agents. It considers both the current concession behavior of the proposing agent and the concession offered by its opponent in the last counteroffer to create a new offer. The proposed mechanism is a kind of imitating offer generation tactic. The difference is that it uses the first order difference between the two last counteroffers received from the opponent as its current reservation value which is one of the important inputs in generating a new offer. In this paper, a bilateral negotiation over a single issue is considered where agents have adverse interests over the issue such as price. Four negotiation environmental settings are used to test the proposed offer generating mechanism. The experimental results show that the proposed hybrid concession mechanism outperforms the time-dependent concession tactic in terms of utility rate while performing lower in one negotiation environment and similarly in most of negotiation environments.

A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.

A new agent-based simulation model of bilateral negotiation

Purpose The purpose of this paper is to introduce a new agent-based simulation model of bilateral negotiation based on a synthesis of established theories and empirical studies of negotiation research. The central units of the model are negotiators who pursue goals, have attributes (trust, assertiveness, cooperativeness, creativity, time, etc.) and perform actions (proposing and accepting offers, exchanging information, creating value, etc). Design/methodology/approach Methodologically, the model follows the agent-based approach to modeling. This approach is chosen because negotiations can be described as complex, non-linear systems involving autonomous agents (i.e. the negotiators), who interact with each other, pursue goals and perform actions aimed at achieving their goals. Findings This paper illustrates how the model can simulate experiments involving variables such as negotiation strategy, creativity, reservation value or time in negotiation. An example simulation is presented which investigates the main and interaction effects of negotiators’ reservation value and their time available for a negotiation. A software implementation of the model is freely accessible at https://tinyurl.com/y7oj6jo8. Research limitations/implications The model, as developed at this point, provides the basis for future research projects. One project could address the representation of emotions and their impact on the process and outcome of negotiations. Another project could extend the model by allowing negotiators to convey false information (i.e. to bluff). Yet another project could be aimed at refining the routines used for making and accepting offers with a view to allow parties to reach partial settlements during a negotiation. Practical implications Due to its broad scope and wide applicability, the model can be used by practitioners and researchers alike. As a decision-support system, the model allows users to simulate negotiation situations and estimate the likelihood of negotiation outcomes. As a research platform, it can generate simulation data in a cost- and time-effective way, allowing researchers to simulate complex, large-N studies at no cost or time. Originality/value The model presented in this paper synthesizes in a novel way a comprehensive range of concepts and theories of current negotiation research. It complements other computational models, in that it can simulate a more diverse range of negotiation strategies (distributive, integrative and compromise) and is applicable to a greater variety of negotiation scenarios.