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Complex negotiations have been conducted for a long time, although until somewhat recently analysts had yet to conceptualize their fundamental nature, their essential elements, and the relationship between these elements. Over the past forty years, however, scholars have gained increasing understanding of the forces that shape negotiation complexity. In this article, the author first reviews literature that has explored complex negotiations, which is found primarily in negotiation studies, and studies of international negotiation. He then develops a five-part theoretical framework for analyzing complex negotiations: 1. identification of negotiation architecture, 2. context analysis, 3. process analysis, 4. structural and relational analysis, and 5. decisional analysis. He then demonstrates the utility of this five-part framework by examining the US-Australia Free Trade negotiations that produced the Australia-US Free Trade Agreement of 2005. Finally, the article closes with some observations on complex negotiations and their analysis.

Inter-firm negotiations are often non-discrete: they are not isolated exchange episodes, but part of an ongoing process of building strategic relationships between the firms. This view posits a challenge to the design and application of negotiation mechanisms in e-markets supporting business-to-business exchanges. The assessment of the mechanisms needs to include both the impacts within each discrete exchange episode and those shaping the future of the inter-firm relationships. We argue that strategies of information revelation implemented with negotiation mechanisms can influence participants’ fairness perceptions, which can further affect business relationships. The paper examines both substantive and subjective measures in an experiment involving two multi-bilateral negotiations with distinct information disclosure strategies. The results show that the revelation of the best offer affects participants’ perceived fairness which, in turn, positively influences intention to do business. The findings have implications for both the design and use of negotiation mechanisms where inter-firm relationships are concerned.

Reliable access to electricity is still a challenge in many developing countries. Indeed, rural areas in sub-Saharan Africa and developing countries such as India still encounter frequent power outages. Local energy markets (LEMs) have emerged as a low-cost solution enabling prosumers with power supply systems such as solar PV to sell their surplus of energy to other members of the local community. This paper proposes a one-to-one automated negotiation framework for peer-to-peer (P2P) local trading of electricity. Our framework uses an autonomous agent model to capture the preferences of both an electricity seller (consumer) and buyer (small local generator or prosumer), in terms of price and electricity quantities to be traded in different periods throughout a day. We develop a bilateral negotiation framework based on the well-known Rubinstein alternating offers protocol, in which the quantity of electricity and the price for different periods are aggregated into daily packages and negotiated between the buyer and seller agent. The framework is then implemented experimentally, with buyers and sellers adopting different negotiation strategies based on negotiation concession algorithms, such as linear heuristic or Boulware. Results show that this framework and agents modelling allow prosumers to increase their revenue while providing electricity access to the community at low cost.

The paper discusses experiments aimed at comparing multi-attribute reverse auctions and multi-bilateral negotiations for procuring goods with multiple attributes. Both exchange mechanisms involve a buyer purchasing from one of several sellers. Two types of negotiations are considered: verifiable and non-verifiable. They differ in the sellers’ knowledge of the current best offer on the table; in verifiable negotiations the best offer is automatically shown to every participant, which makes it similar to auctions. Online auctions and negotiation systems were used to study auction and negotiation processes, and the mechanisms’ efficiency. The results show that buyers did best using auctions, followed by non-verifiable and verifiable negotiations. We also looked into the differences between auctions and negotiations in terms of their duration, sellers’ and buyers’ involvement, and efficiency and conclude that the behavior of buyers and sellers cannot be explained solely on the grounds of traditional economics. It can, however, be explained on the grounds of social exchange theory and behavioral economics. In multi-bilateral negotiations competition and social behavior coexist. When transparency is introduced the social effect becomes stronger, weakening the impact of competition.

The ubiquitous coverage/connectivity requirement of wireless cellular networks has shifted mobile network operators’ (MNOs) interest toward dense deployment of small cells with coverage areas that are much smaller as compared to macrocell base stations (MBSs). Multi-operator small cells could provide virtualization of network resources (infrastructure and spectrum) and enable its efficient utilization, i.e., uninterrupted coverage and connectivity to subscribers, and an opportunity to avoid under-utilization of the network resources. However, a MNO with exclusive ownership to network resources would have little incentive to utilize its precious resources to serve users of other MNOs, since MNOs differentiate among others based on their ownership of the licensed spectrum. Thus, considering network resources scarcity and under-utilization, this paper proposes a mechanism for multi-operator small cells collaboration through negotiation that establishes a mutual agreement acceptable to all involved parties, i.e., a win–win situation for the collaborating MNOs. It enables subscribers of a MNO to utilize other MNOs’ network resources, and allows MNOs to offer small cells “as a service” to users with ubiquitous access to wireless coverage/connectivity, maximize the use of an existing network resources by serving additional users from a market share, and enhance per-user data rate. We validated and evaluated the proposed mechanism through simulations considering various performance metrics.

Negotiations between neighbours over river disputes are not only about water, they come embedded with other bilateral concerns. Thus, the solution of a river dispute depends on comparative bargaining capabilities of riparians on their many other contentious matters. This assumption has been applied in investigating and analysing the Ganges River negotiation between India and Bangladesh. The literature on the Ganges has focussed on integrated river development perspectives. The present analysis underlines the convergence of water with other bilateral concerns. The examination of a quarter-century negotiation on the Ganges confirms the correlation between positive outcomes and better linkages between multiple issues.

This article examines the role of interpersonal trust (IPT) in establishing and developing the 1992–1993 Oslo Channel by drawing on a number of interviews alongside available English-language discursive material. The central argument is that IPT developed in two specific dyadic relationships in the Oslo Channel: between Dr. Yair Hirschfeld and Ahmed Qurei, and then between Qurei and Uri Savir. These relationships underpinned the development of the Oslo Channel. To support this argument, I examine the 1991–1993 Washington negotiations and demonstrate that IPT did not develop there. To analyze the relationships discussed in this article, I draw extensively on interdisciplinary literature on trust, examining how the basis for IPT changes in these dyads as the relationships develop. This article complements the existing literature on the Oslo Channel by examining the relationships between key individuals on the Israeli and Palestinian side.

Autonomous agents can negotiate on behalf of buyers and sellers to make a contract in the e-marketplace. In bilateral negotiation, they need to find a joint agreement by satisfying each other. That is, an agent should learn its opponent's preferences. However, the agent has limited time to find an agreement while trying to protect its payoffs by keeping its preferences private. In doing so, generating offers with incomplete information about the opponent's preferences is a complex process and, therefore, learning these preferences in a short time can assist the agent to generate proper offers. In this paper, we have developed an incremental on-line learning approach by using a hybrid soft-computing technique to learn the opponent's preferences. In our learning approach, first, the size of possible preferences is reduced by encoding the uncertain preferences into a series of fuzzy membership functions. Then, a simplified genetic algorithm is used to search the best fuzzy preferences that articulate the opponent's intention. Experimental results showed that our learning approach can estimate the opponent's preferences effectively. Moreover, results indicate that agents which use the proposed learning approach not only have more chances to reach agreements but also will be able to find agreements with greater joint utility. [PUBLICATION ABSTRACT]

Automated negotiation about price and quantity is increasingly common in e-commerce. This paper presents an automated bilateral negotiation conducted by labor-saving and emotion-free autonomous agents. An automated negotiation may alleviate the difficulties inherent in human negotiations. To avoid a deadlock in which both agents are unwilling to concede may arise in the traditional alternating-offer pattern, a mediator agent is introduced. After outlining the negotiation workflow, we aim to analyze the correlation of price and quantity in a bilateral negotiation in different situations; then propose the negotiation methods how negotiate price and quantity for different situations. We hope the research can offer the guide for applying the proposed method to the e-commerce.