Acceptance conditions in automated negotiation

In every negotiation with a deadline, one of the negotiating parties has to accept an offer to avoid a break off. A break off is usually an undesirable outcome for both parties, therefore it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When designing such conditions one is faced with the acceptance dilemma: accepting the current offer may be suboptimal, as better offers may still be presented. 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. Motivated by the challenges of bilateral negotiations between automated agents and by the results and insights of the automated negotiating agents competition (ANAC), we classify and compare state-of-the-art generic acceptance conditions. We focus on decoupled acceptance conditions, i.e. conditions that do not depend on the bidding strategy that is used. We performed extensive experiments to compare the performance of acceptance conditions in combination with a broad range of bidding strategies and negotiation domains. Furthermore we propose new acceptance conditions and we demonstrate that they outperform the other conditions that we study. In particular, it is shown that they outperform the standard acceptance condition of comparing the current offer with the offer the agent is ready to send out. We also provide insight in to why some conditions work better than others and investigate correlations between the properties of the negotiation environment and the efficacy of acceptance condition

What to bid and when to stop

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

Practical strategies for agent-based negotiation in complex environments

Agent-based negotiation, whereby the negotiation is automated by software programs, can be applied to many different negotiation situations, including negotiations between friends, businesses or countries. A key benefit of agent-based negotiation over human negotiation is that it can be used to negotiate effectively in complex negotiation environments, which consist of multiple negotiation issues, time constraints, and multiple unknown opponents. While automated negotiation has been an active area of research in the past twenty years, existing work has a number of limitations. Specifically, most of the existing literature has considered time constraints in terms of the number of rounds of negotiation that take place. In contrast, in this work we consider time constraints which are based on the amount of time that has elapsed. This requires a different approach, since the time spent computing the next action has an effect on the utility of the outcome, whereas the actual number of offers exchanged does not. In addition to these time constraints, in the complex negotiation environments which we consider, there are multiple negotiation issues, and we assume that the opponents’ preferences over these issues and the behaviour of those opponents are unknown. Finally, in our environment there can be concurrent negotiations between many participants.Against this background, in this thesis we present the design of a range of practical negotiation strategies, the most advanced of which uses Gaussian process regression to coordinate its concession against its various opponents, whilst considering the behaviour of those opponents and the time constraints. In more detail, the strategy uses observations of the offers made by each opponent to predict the future concession of that opponent. By considering the discounting factor, it predicts the future time which maximises the utility of the offers, and we then use this in setting our rate of concession.Furthermore, we evaluate the negotiation agents that we have developed, which use our strategies, and show that, particularly in the more challenging scenarios, our most advanced strategy outperforms other state-of-the-art agents from the Automated Negotiating Agent Competition, which provides an international benchmark for this work. In more detail, our results show that, in one-to-one negotiation, in the highly discounted scenarios, our agent reaches outcomes which, on average, are 2.3% higher than those of the next best agent. Furthermore, using empirical game theoretic analysis we show the robustness of our strategy in a variety of tournament settings. This analysis shows that, in the highly discounted scenarios, no agent can benefit by choosing a different strategy (taken from the top four strategies in that setting) than ours. Finally, in the many-to-many negotiations, we show how our strategy is particularly effective in highly competitive scenarios, where it outperforms the state-of-the-art many-to-many negotiation strategy by up to 45%

Extrinsic Rewards and Intrinsic Motives: Standard and Behavioral Approaches to Agency and Labor Markets

Employers structure pay and employment relationships to mitigate agency problems. A large literature in economics documents how the resolution of these problems shapes personnel policies and labor markets. For the most part, the study of agency in employment relationships relies on highly stylized assumptions regarding human motivation, e.g., that employees seek to earn as much money as possible with minimal effort. In this essay, we explore the consequences of introducing behavioral complexity and realism into models of agency within organizations. Specifically, we assess the insights gained by allowing employees to be guided by such motivations as the desire to compare favorably to others, the aspiration to contribute to intrinsically worthwhile goals, and the inclination to reciprocate generosity or exact retribution for perceived wrongs. More provocatively, from the standpoint of standard economics, we also consider the possibility that people are driven, in ways that may be opaque even to themselves, by the desire to earn social esteem or to shape and reinforce identity.agency, motivation, employment relationships, behavioral economics

The first automated negotiating agents competition (ANAC 2010)

Motivated by the challenges of bilateral negotiations between people and automated agents we organized the first automated negotiating agents competition (ANAC 2010). The purpose of the competition is to facilitate the research in the area bilateral multi-issue closed negotiation. The competition was based on the Genius environment, which is a General Environment for Negotiation with Intelligent multi-purpose Usage Simulation. The first competition was held in conjunction with the Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-10) and was comprised of seven teams. This paper presents an overview of the competition, as well as general and contrasting approaches towards negotiation strategies that were adopted by the participants of the competition. Based on analysis in post--tournament experiments, the paper also attempts to provide some insights with regard to effective approaches towards the design of negotiation strategies

Why agents for automated negotiations should be adaptive

We show that adaptive agents on the Internet can learn to exploit bidding agents who use a (limited) number of fixed strategies. These learning agents can be generated by adapting a special kind of finite automata with evolutionary algorithms (EAs). Our approach is especially powerful if the adaptive agent participates in frequently occurring micro-transactions, where there is sufficient opportunity for the agent to learn online from past negotiations. More in general, results presented in this paper provide a solid basis for the further development of adaptive agents for Internet applications

The effectiveness of alternative programs of management by objectives on firm performance : an experimental study

The research focuses on the use of laboratory experimentation in order to design an effective MBO program for the executives of the major Italian electric utility provider (Enel). A game theoretical formulation of the MBO program currently adopted by Enel is given (baseline model) and laboratory experiments are implemented to test (a) how experimental subjects behave within the stylized MBO program and (b) the relative effectiveness on subjects performance of the introduction of two different institutions (liability and tournament rules) governing the assignment of incentives. Results highlight that: 1) the baseline model resembles what has been observed in the field study: targets negotiated between principals and agents are easy to fulfill and a large majority of executives reach their own prize. 2) Liability rules increase the bargaining position of principals and result overall in higher levels of target assigned to agents and corresponding higher levels of performance of them; conversely no savings are observed in terms of cost of the MBO program. 3) Tournament rules allow to save more in MBO program costs; on the other side performance are, on average, similar to the baseline and variance is higher since the population splits into two sub{groups where a majority of agents shows performance levels close to the case of the liability rule while a minority of them shirk and decrease their working effort until the minimal performance. Some indications on the use of laboratory experimentation as a tool of organizational design are finally suggested