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Journal ArticleDOI

An Adaptive Agent for Negotiating with People in Different Cultures

TL;DR: A new agent for repeated bilateral negotiation that was designed to model and adapt its behavior to the individual traits exhibited by its negotiation partner, showing that adaptation is a viable approach towards the design of computer agents to negotiate with people when there is no prior data of their behavior.
Abstract: The rapid dissemination of technology such as the Internet across geographical and ethnic lines is opening up opportunities for computer agents to negotiate with people of diverse cultural and organizational affiliations. To negotiate proficiently with people in different cultures, agents need to be able to adapt to the way behavioral traits of other participants change over time. This article describes a new agent for repeated bilateral negotiation that was designed to model and adapt its behavior to the individual traits exhibited by its negotiation partner. The agent’s decision-making model combined a social utility function that represented the behavioral traits of the other participant, as well as a rule-based mechanism that used the utility function to make decisions in the negotiation process. The agent was deployed in a strategic setting in which both participants needed to complete their individual tasks by reaching agreements and exchanging resources, the number of negotiation rounds was not fixed in advance and agreements were not binding. The agent negotiated with human subjects in the United States and Lebanon in situations that varied the dependency relationships between participants at the onset of negotiation. There was no prior data available about the way people would respond to different negotiation strategies in these two countries. Results showed that the agent was able to adopt a different negotiation strategy to each country. Its average performance across both countries was equal to that of people. However, the agent outperformed people in the United States, because it learned to make offers that were likely to be accepted by people, while being more beneficial to the agent than to people. In contrast, the agent was outperformed by people in Lebanon, because it adopted a high reliability measure which allowed people to take advantage of it. These results provide insight for human-computer agent designers in the types of multicultural settings that we considered, showing that adaptation is a viable approach towards the design of computer agents to negotiate with people when there is no prior data of their behavior.
Citations
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Book
13 Nov 2014
TL;DR: This state-of-the-art treatment of the subject explores key issues involved in the design of negotiating agents, covering strategic, heuristic, and axiomatic approaches.
Abstract: With an increasing number of applications in the context of multi-agent systems, automated negotiation is a rapidly growing area. Written by top researchers in the field, this state-of-the-art treatment of the subject explores key issues involved in the design of negotiating agents, covering strategic, heuristic, and axiomatic approaches. The authors discuss the potential benefits of automated negotiation as well as the unique challenges it poses for computer scientists and for researchers in artificial intelligence. They also consider possible applications and give readers a feel for the types of domains where automated negotiation is already being deployed. This book is ideal for graduate students and researchers in computer science who are interested in multi-agent systems. It will also appeal to negotiation researchers from disciplines such as management and business studies, psychology and economics.

98 citations

Journal ArticleDOI
TL;DR: This paper identifies a range of social attributes in an open-network setting that influence people's decision-making and thus affect the performance of computer-agent strategies, and establishes the importance of learning and adaptation to the success of such strategies.

78 citations


Cites background from "An Adaptive Agent for Negotiating w..."

  • ...These rules are based in part on a decision-making model designed to adapt to people’s negotiation behavior in different cultures [Gal et al., 2010, to appear]....

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DissertationDOI
18 Sep 2014
TL;DR: TheBOA framework leads to significant improvements in agent design by wining ANAC 2013, which had 19 participating teams from 8 international institutions, with an agent that is designed using the BOA framework and is informed by a preliminary analysis of the different components.
Abstract: Negotiation is an important activity in human society, and is studied by various disciplines, ranging from economics and game theory, to electronic commerce, social psychology, and artificial intelligence. Traditionally, negotiation is a necessary, but also time-consuming and expensive activity. Therefore, in the last decades there has been a large interest in the automation of negotiation, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents eventually being able to negotiate on behalf of human negotiators. Every year, automated negotiation agents are improving in various ways, and there is now a large body of negotiation strategies available, all with their unique strengths and weaknesses. For example, some agents are able to predict the opponent's preferences very well, while others focus more on having a sophisticated bidding strategy. The problem however, is that there is little incremental improvement in agent design, as the agents are tested in varying negotiation settings, using a diverse set of performance measures. This makes it very difficult to meaningfully compare the agents, let alone their underlying techniques. As a result, we lack a reliable way to pinpoint the most effective components in a negotiating agent. There are two major advantages of distinguishing between the different components of a negotiating agent's strategy: first, it allows the study of the behavior and performance of the components in isolation. For example, it becomes possible to compare the preference learning component of all agents, and to identify the best among them. Second, we can proceed to mix and match different components to create new negotiation strategies., e.g.: replacing the preference learning technique of an agent and then examining whether this makes a difference. Such a procedure enables us to combine the individual components to systematically explore the space of possible negotiation strategies. To develop a compositional approach to evaluate and combine the components, we identify structure in most agent designs by introducing the BOA architecture, in which we can develop and integrate the different components of a negotiating agent. We identify three main components of a general negotiation strategy; namely a bidding strategy (B), possibly an opponent model (O), and an acceptance strategy (A). The bidding strategy considers what concessions it deems appropriate given its own preferences, and takes the opponent into account by using an opponent model. The acceptance strategy decides whether offers proposed by the opponent should be accepted. The BOA architecture is integrated into a generic negotiation environment called Genius, which is a software environment for designing and evaluating negotiation strategies. To explore the negotiation strategy space of the negotiation research community, we amend the Genius repository with various existing agents and scenarios from literature. Additionally, we organize a yearly international negotiation competition (ANAC) to harvest even more strategies and scenarios. ANAC also acts as an evaluation tool for negotiation strategies, and encourages the design of negotiation strategies and scenarios. We re-implement agents from literature and ANAC and decouple them to fit into the BOA architecture without introducing any changes in their behavior. For each of the three components, we manage to find and analyze the best ones for specific cases, as described below. We show that the BOA framework leads to significant improvements in agent design by wining ANAC 2013, which had 19 participating teams from 8 international institutions, with an agent that is designed using the BOA framework and is informed by a preliminary analysis of the different components. In every negotiation, one of the negotiating parties must accept an offer to reach an agreement. Therefore, it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When contemplating whether to accept an offer, the agent is faced with the acceptance dilemma: accepting the offer may be suboptimal, as better offers may still be presented before time runs out. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. We classify and compare state-of-the-art generic acceptance conditions. We propose new acceptance strategies and we demonstrate that they outperform the other conditions. We also provide insight into why some conditions work better than others and investigate correlations between the properties of the negotiation scenario and the efficacy of acceptance conditions. Later, we adopt a more principled approach by applying optimal stopping theory to calculate the optimal decision on the acceptance of an offer. We approach the decision of whether to accept as a sequential decision problem, by modeling the bids received as a stochastic process. We determine the optimal acceptance policies for particular opponent classes and we present an approach to estimate the expected range of offers when the type of opponent is unknown. We show that the proposed approach is able to find the optimal time to accept, and improves upon all existing acceptance strategies. Another principal component of a negotiating agent's strategy is its ability to take the opponent's preferences into account. The quality of an opponent model can be measured in two different ways. One is to use the agent's performance as a benchmark for the model's quality. We evaluate and compare the performance of a selection of state-of-the-art opponent modeling techniques in negotiation. We provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. We identify a class of simple and surprisingly effective opponent modeling techniques that did not receive much previous attention in literature. The other way to measure the quality of an opponent model is to directly evaluate its accuracy by using similarity measures. We review all methods to measure the accuracy of an opponent model and we then analyze how changes in accuracy translate into performance differences. Moreover, we pinpoint the best predictors for good performance. This leads to new insights concerning how to construct an opponent model, and what we need to measure when optimizing performance. Finally, we take two different approaches to gain more insight into effective bidding strategies. We present a new classification method for negotiation strategies, based on their pattern of concession making against different kinds of opponents. We apply this technique to classify some well-known negotiating strategies, and we formulate guidelines on how agents should bid in order to be successful, which gives insight into the bidding strategy space of negotiating agents. Furthermore, we apply optimal stopping theory again, this time to find the concessions that maximize utility for the bidder against particular opponents. We show there is an interesting connection between optimal bidding and optimal acceptance strategies, in the sense that they are mirrored versions of each other. Lastly, after analyzing all components separately, we put the pieces back together again. We take all BOA components accumulated so far, including the best ones, and combine them all together to explore the space of negotiation strategies. We compute the contribution of each component to the overall negotiation result, and we study the interaction between components. We find that combining the best agent components indeed makes the strongest agents. This shows that the component-based view of the BOA architecture not only provides a useful basis for developing negotiating agents but also provides a useful analytical tool. By varying the BOA components we are able to demonstrate the contribution of each component to the negotiation result, and thus analyze the significance of each. The bidding strategy is by far the most important to consider, followed by the acceptance conditions and finally followed by the opponent model. Our results validate the analytical approach of the BOA framework to first optimize the individual components, and then to recombine them into a negotiating agent.

71 citations


Cites background from "An Adaptive Agent for Negotiating w..."

  • ...Another important direction is the design of agents that perform well not only against other automated strategies, but also against human opponents [93, 161, 162, 246]....

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Proceedings ArticleDOI
02 May 2011
TL;DR: The agent was shown to outperform people as well as agents playing the equilibrium strategy of the game in empirical studies spanning hundreds of subjects, and be more likely to reach agreement than people or agents playing equilibrium strategies.
Abstract: Revelation games are bilateral bargaining games in which agents may choose to truthfully reveal their private information before engaging in multiple rounds of negotiation. They are analogous to real-world situations in which people need to decide whether to disclose information such as medical records or university transcripts when negotiating over health plans and business transactions. This paper presents an agent-design that is able to negotiate proficiently with people in a revelation game with different dependencies that hold between players. The agent modeled the social factors that affect the players' revelation decisions on people's negotiation behavior. It was empirically shown to outperform people in empirical evaluations as well as agents playing equilibrium strategies. It was also more likely to reach agreement than people or equilibrium agents.

62 citations

Journal ArticleDOI
22 Jul 2012
TL;DR: This work demonstrates the efficacy of combining computational methods with behavioral economics to model how people reason about machine-generated advice and presents a general methodology for agent-design in such repeated advice settings.
Abstract: This paper addresses the problem of automated advice provision in scenarios that involve repeated interactions between people and computer agents. This problem arises in many applications such as route selection systems, office assistants and climate control systems. To succeed in such settings agents must reason about how their advice influences people's future actions or decisions over time. This work models such scenarios as a family of repeated bilateral interaction called "choice selection processes", in which humans or computer agents may share certain goals, but are essentially self-interested. We propose a social agent for advice provision (SAP) for such environments that generates advice using a social utility function which weighs the sum of the individual utilities of both agent and human participants. The SAP agent models human choice selection using hyperbolic discounting and samples the model to infer the best weights for its social utility function. We demonstrate the effectiveness of SAP in two separate domains which vary in the complexity of modeling human behavior as well as the information that is available to people when they need to decide whether to accept the agent's advice. In both of these domains, we evaluated SAP in extensive empirical studies involving hundreds of human subjects. SAP was compared to agents using alternative models of choice selection processes informed by behavioral economics and psychological models of decision-making. Our results show that in both domains, the SAP agent was able to outperform alternative models. This work demonstrates the efficacy of combining computational methods with behavioral economics to model how people reason about machine-generated advice and presents a general methodology for agent-design in such repeated advice settings.

54 citations


Cites background from "An Adaptive Agent for Negotiating w..."

  • ...According to the social preference theory, people consider others’ outcomes as well as their own when making strategic decisions [44]....

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References
More filters
Book
20 Apr 2001
TL;DR: In this paper, values and culture data collection, treatment and validation power distance Uncertainty Avoidance Individualism and Collectivism Masculinity and Femininity Long versus Short-Term Orientation Cultures in Organizations Intercultural Encounters Using Culture Dimension Scores in Theory and Research
Abstract: Values and Culture Data Collection, Treatment and Validation Power Distance Uncertainty Avoidance Individualism and Collectivism Masculinity and Femininity Long versus Short-Term Orientation Cultures in Organizations Intercultural Encounters Using Culture Dimension Scores in Theory and Research

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Journal ArticleDOI
TL;DR: The authors reviewed the book "Culture's Consequences: Comparing Values, Behaviors, Institutions, and Organizations Across Nations, Second Edition, by Geert Hofstede".
Abstract: The article reviews the book “Culture's Consequences: Comparing Values, Behaviors, Institutions, and Organizations Across Nations,” Second Edition, by Geert Hofstede.

6,062 citations

Book
17 Mar 2003
TL;DR: The first substantial and authoritative effort to close this gap was made by Camerer, who used psychological principles and hundreds of experiments to develop mathematical theories of reciprocity, limited strategizing, and learning, which help predict what real people and companies do in strategic situations as discussed by the authors.
Abstract: Game theory, the formalized study of strategy, began in the 1940s by asking how emotionless geniuses should play games, but ignored until recently how average people with emotions and limited foresight actually play games. This book marks the first substantial and authoritative effort to close this gap. Colin Camerer, one of the field's leading figures, uses psychological principles and hundreds of experiments to develop mathematical theories of reciprocity, limited strategizing, and learning, which help predict what real people and companies do in strategic situations. Unifying a wealth of information from ongoing studies in strategic behavior, he takes the experimental science of behavioral economics a major step forward. He does so in lucid, friendly prose. Behavioral game theory has three ingredients that come clearly into focus in this book: mathematical theories of how moral obligation and vengeance affect the way people bargain and trust each other; a theory of how limits in the brain constrain the number of steps of "I think he thinks . . ." reasoning people naturally do; and a theory of how people learn from experience to make better strategic decisions. Strategic interactions that can be explained by behavioral game theory include bargaining, games of bluffing as in sports and poker, strikes, how conventions help coordinate a joint activity, price competition and patent races, and building up reputations for trustworthiness or ruthlessness in business or life. While there are many books on standard game theory that address the way ideally rational actors operate, Behavioral Game Theory stands alone in blending experimental evidence and psychology in a mathematical theory of normal strategic behavior. It is must reading for anyone who seeks a more complete understanding of strategic thinking, from professional economists to scholars and students of economics, management studies, psychology, political science, anthropology, and biology.

4,701 citations

Journal ArticleDOI
TL;DR: This paper found that subjects are more concerned with increasing social welfare, sacrificing to increase the payoffs for all recipients, especially low-payoff recipients, than with reducing differences in payoffs.
Abstract: Departures from self-interest in economic experiments have recently inspired models of “social preferences” We design a range of simple experimental games that test these theories more directly than existing experiments Our experiments show that subjects are more concerned with increasing social welfare—sacrificing to increase the payoffs for all recipients, especially low-payoff recipients—than with reducing differences in payoffs (as supposed in recent models) Subjects are also motivated by reciprocity: They withdraw willingness to sacrifice to achieve a fair outcome when others are themselves unwilling to sacrifice, and sometimes punish unfair behavior

2,984 citations

Posted Content
TL;DR: In an experiment comparing two-person bargaining and multiperson markets in Israel, Japan, United States, and Yugoslavia, market outcomes converged to equilibrium everywhere, with no payoff-relevant differences between countries.
Abstract: In an experiment comparing two-person bargaining and multiperson markets in Israel, Japan, the United States, and Yugoslavia, market outcomes converged to equilibrium everywhere, with no payoff-relevant differences between countries Bargaining outcomes were everywhere different from equilibrium predictions (both in agreements and in the substantial frequency of disagreements) and differences were observed between countries Because of the experimental design, the fact that the market behavior is the same in all countries supports the hypothesis that the observed differences are not due to differences in languages, currencies, or experimenters, but may tentatively be attributed to cultural differences Copyright 1991 by American Economic Association (This abstract was borrowed from another version of this item)

1,203 citations