Predicting opponent’s moves in electronic negotiations using neural networks

doi:10.1016/j.eswa.2006.12.027

Expert Systems with Applications

Volume 34, Issue 2, February 2008, Pages 1266-1273

https://doi.org/10.1016/j.eswa.2006.12.027 Get rights and content

Abstract

Electronic negotiation experiments provide a rich source of information about relationships between the negotiators, their individual actions, and the negotiation dynamics. This information can be effectively utilized by intelligent agents equipped with adaptive capabilities to learn from past negotiations and assist in selecting appropriate negotiation tactics. This paper presents an approach to modeling the negotiation process in a time-series fashion using artificial neural network. In essence, the network uses information about past offers and the current proposed offer to simulate expected counter-offers. On the basis of the model’s prediction, “what-if” analysis of counter-offers can be done with the purpose of optimizing the current offer. The neural network has been trained using the Levenberg–Marquardt algorithm with Bayesian Regularization. The simulation of the predictive model on a testing set has very good and highly significant performance. The findings suggest that machine learning techniques may find useful applications in the context of electronic negotiations. These techniques can be effectively incorporated in an intelligent agent that can sense the environment and assist negotiators by providing predictive information, and possibly automating some negotiation steps.

Introduction

Negotiations play a crucial role in conducting everyday business activities, such as negotiating contracts with customers, negotiating service level agreements with suppliers or negotiating agreements with unions. Many of these negotiations can have outcomes that impact long-term business relationships, profitability, and reputation of businesses. Electronic negotiations in particular have gained heightened importance due to the advance of the web and e-commerce (Kersten & Noronha, 1999). While this brings in the challenges associated with conducting negotiations in the global environment, where parties could have little or no knowledge of each other, it also presents some valuable opportunities to employ advanced technologies, such as intelligent agents, in the negotiation process.

Intelligent systems for negotiation support that aim at enhancing the negotiator’s abilities to understand the counterparts, their needs and limitations and to predict their moves could be very valuable tools to be used in negotiation tasks (Zeng & Sycara, 1998). The purpose of this paper is to investigate the feasibility of machine learning approaches in modeling the opponent’s future offers. The data are obtained from electronic negotiation experiments which provide a rich source of information about the relationships between negotiators, their individual actions, and the negotiation dynamics. This information can be effectively utilized by intelligent agents equipped with adaptive capabilities to learn from past negotiations and assist in selecting appropriate negotiation tactics.

In order to test the applicability of machine learning approaches in modeling negotiation dynamics, we use data obtained from bilateral negotiations experiments conducted with the use of the Inspire electronic negotiation system (Kersten and Lo, 2003, Kersten and Noronha, 1999). In our approach, the negotiation process has been modeled in a time-series fashion using an artificial neural network. In essence, the model uses information about past offers and counteroffers, including the most recent offer made by the negotiator, to predict the expected counteroffer. On the basis of the model’s prediction, “what-if” analysis of counter-offers can be done with the purpose of optimizing the current offer. The assessment of offers and counter-offers is performed based on the user’s utility function.

The purpose of this study is to assess the applicability of machine learning to provide advice to the negotiator. This advice involves simulation of the possible responses to the offer the negotiator is contemplating. The subsequent sections present the related work; introduce a neural network-based approach to model the negotiator’s counterpart; propose negotiation support which involves offering optimization; discuss the model implementation and results; and provide conclusions and directions for future work.

Section snippets

Background

Negotiation is one of the key activities of businesses and it has a crucial impact on an organization’s performance. Researchers in negotiation support and automation seek to facilitate various negotiation-related tasks using the capabilities of technology. Substantial efforts have been expended in attempts to fully automate negotiation processes (Beam and Segev, 1996, Chavez et al., 1997a, Jennings and Faratin, 2001, Maes et al., 1998). While the extensive coverage of automated negotiations is

Neural network-based predictive model

Modeling of an opponent in the negotiation process may significantly improve performance of the negotiators. Some of the works mentioned above attempt to incorporate the opponent’s moves in the process of offer generation. For example, in Lee (2004) past concessions made by the counterpart are used to construct the model of this counterpart. If, on the average, they exceed a pre-defined threshold level, the opponent is modeled as having a “positive” attitude. Zeng and Sycara (1998) use

Negotiation support with predictive neural model

The predictive neural network-based model introduced in the previous section can be integrated as part of the negotiation support system. One concern with such integration relates to the informational demand required by the model. Ideally, the model should not require extensive information to facilitate the “plug-in” type of integration. Thus, when building the model, we kept a strong focus on using only information that would normally be available to the negotiator.

The most common type of

Model implementation

In order to demonstrate the feasibility and effectiveness of an ANN-based predictive model, we have used past data collected by Inspire system (Kersten & Noronha, 1999). The Inspire negotiation system is web-based, which permits two parties located anywhere in the world, who have internet access, to negotiate on a chosen case. Negotiation issues and issue options are specified in advance for a specific case and each negotiator specifies a rating for each issue and, additionally, specifies a

Results

Training of the neural network was executed and the networks converged after 252 iterations through all of the dataset. Convergence is characterized by the stabilization of the sum of square errors (SSE) and the sum of square weights (SSW). For our network of 39 inputs, 10 hidden layer neurons and 4 outputs, we reach a stable SSE of 3642.17, a SSW of 39.5911 and an effective number of network parameters (weight and biases) of 382.143 of the total 444 parameters (Fig. 2).

The correlations of the

Summary and conclusions

In this paper, we have presented a neural networked-based model for predicting the opponent’s offers during negotiation process. The model can be embedded in a system for assisting the negotiator in making offers in e-negotiations. The simulation of the predictive model on a testing set has very good and highly significant performance, especially considering the noisy data domain. An examination of “what-if” scenarios and optimization results on a real case shows that the model can exhibit

Acknowledgements

This work has been partially supported with grants from the Initiative for New Economy of the Natural Sciences and Engineering Research Council and the Social Sciences and Humanities Research Council, Canada. An earlier version of the work has been presented at the Group Decision and Negotiation Conference, Karlsruhe, Germany, June 25–29, 2006.

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Predicting opponent’s moves in electronic negotiations using neural networks

Abstract

Introduction

Section snippets

Background

Neural network-based predictive model

Negotiation support with predictive neural model

Model implementation

Results

Summary and conclusions

Acknowledgements

Expert Systems with Applications

Artificial Intelligence

Neural Networks

Decision Support Systems

Expert Systems with Applications

Expert Systems with Applications

International Journal of Human-Computer Studies

Automated negotiation: A survey and the state of art

A real-life experiment in creating an agent marketplace

Agent-supported negotiations in the e-marketplace

International Journal of Electronic Business