Comparing mind perception in strategic exchanges: human-agent negotiation, dictator and ultimatum games

Recent research shows that how we respond to other social actors depends on what sort of mind we ascribe to them. In a comparative manner, we observed how perceived minds of agents shape people’s behavior in the dictator game, ultimatum game, and negotiation against artificial agents. To do so, we varied agents’ minds on two dimensions of the mind perception theory: agency (cognitive aptitude) and patiency (affective aptitude) via descriptions and dialogs. In our first study, agents with emotional capacity garnered more allocations in the dictator game, but in the ultimatum game, agents’ described agency and affective capacity, both led to greater offers. In the second study on negotiation, agents ascribed with low-agency traits earned more points than those with high-agency traits, though the negotiation tactic was the same for all agents. Although patiency did not impact game points, participants sent more happy and surprise emojis and emotionally valenced messages to agents that demonstrated emotional capacity during negotiations. Further, our exploratory analyses indicate that people related only to agents with perceived affective aptitude across all games. Both perceived agency and affective capacity contributed to moral standing after dictator and ultimatum games. But after negotiations, only agents with perceived affective capacity were granted moral standing. Manipulating mind dimensions of machines has differing effects on how people react to them in dictator and ultimatum games, compared to a more complex economic exchange like negotiation. We discuss these results, which show that agents are perceived not only as social actors, but as intentional actors through negotiations, in contrast with simple economic games.

Using Automated Agents to Teach Negotiation

Negotiation is an integral part of our daily lives regardless of occupation. Although ubiquitous to our experience, we are never taught to negotiate. This lack of training presents many consequences from unfair salary negotiation to geopolitical ramification. The ability to resolve conflicts and negotiate is becoming more critical due to the rise of automated systems which look to replace various repetitive task jobs. In hopes of improving human negotiation skills, my work seeks to develop automated negotiation agents capable of providing personalized feedback. In this paper, I provide an overview of my past , current, and future work.

The importance of context-dependent learning in negotiation agents

Automated negotiation between artificial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiation agent depends significantly on the influence of environmental conditions or contextual variables, since they affect not only a given agent preferences and strategies, but also those of other agents. Despite this, the existing literature on automated negotiation is scarce about how to properly account for the effect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is introduced. Also, a simple negotiation agent that queries available information from its environment, internally models contextual variables, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes against other negotiation agents in the existing literature, it is shown using our context-aware agent that it makes no sense to negotiate without taking context-relevant variables into account. Our context-aware negotiation agent has been implemented in the GENIUS environment, and results obtained are significant and quite revealing.

When Will Negotiation Agents Be Able to Represent Us? The Challenges and Opportunities for Autonomous Negotiators

Computers that negotiate on our behalf hold great promise for the future and will even become indispensable in emerging application domains such as the smart grid and the Internet of Things. Much research has thus been expended to create agents that are able to negotiate in an abundance of circumstances. However, up until now, truly autonomous negotiators have rarely been deployed in real-world applications. This paper sizes up current negotiating agents and explores a number of technological, societal and ethical challenges that autonomous negotiation systems have brought about. The questions we address are: in what sense are these systems autonomous, what has been holding back their further proliferation, and is their spread something we should encourage? We relate the automated negotiation research agenda to dimensions of autonomy and distill three major themes that we believe will propel autonomous negotiation forward: accurate representation, long-term perspective, and user trust. We argue these orthogonal research directions need to be aligned and advanced in unison to sustain tangible progress in the field.

Quantitative Concession Behavior Analysis and Prediction for Decision Support in Electronic Negotiations

Quantitative analysis of negotiation concession behavior is performed based on empirical data with the purpose of providing simple and intuitive decision support in electronic negotiations. Previous work on non-linear concave preferences and subsequent concession crossover provides a theoretical basis for the model. The authors propose a model which quantifies the remaining concession potential for each issue and a generalization of the model which permits the memory/decay of past concessions. These models permit the analysis of negotiators' concession behavior. Using the proposed models, it was possible to quantitatively determine that negotiators in the authors' negotiation case exhibit concession crossover issues and thus have a tendency to give concessions on issues with the most remaining concession potential. This finding provides empirical evidence of concession crossover in actual concessions and the corresponding model permits the design of a simple and intuitive prediction methodology, which could be used in real world negotiations by decision support systems or automated negotiation agents.

The Automated Negotiating Agents Competition, 2010–2015

The Automated Negotiating Agents Competition is an international event that, since 2010, has contributed to the evaluation and development of new techniques and benchmarks for improving the state-of-the-art in automated multi-issue negotiation. A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, bilateral and multilateral protocols. Two of the challenges that remain are: How to develop argumentation-based negotiation agents that next to bids, can inform and argue to obtain an acceptable agreement for both parties, and how to create agents that can negotiate in a human fashion.

A belief revision approach for argumentation-based negotiation agents

Negotiation is an interaction that happens in multi-agent systems when agents have conflicting objectives and must look for an acceptable agreement. A typical negotiating situation involves two agents that cannot reach their goals by themselves because they do not have some resources they need or they do not know how to use them to reach their goals. Therefore, they must start a negotiation dialogue, taking also into account that they might have incomplete or wrong beliefs about the other agent’s goals and resources. This article presents a negotiating agent model based on argumentation, which is used by the agents to reason on how to exchange resources and knowledge in order to achieve their goals. Agents that negotiate have incomplete beliefs about the others, so that the exchange of arguments gives them information that makes it possible to update their beliefs. In order to formalize their proposals in a negotiation setting, the agents must be able to generate, select and evaluate arguments associated with such offers, updating their mental state accordingly. In our approach, we will focus on an argumentation-based negotiation model between two cooperative agents. The arguments generation and interpretation process is based on belief change operations (expansions, contractions and revisions), and the selection process is a based on a strategy. This approach is presented through a high-level algorithm implemented in logic programming. We show various theoretical properties associated with this approach, which have been formalized and proved using Coq, a formal proof management system. We also illustrate, through a case study, the applicability of our approach in order to solve a slightly modified version of the well-known home improvement agents problem. Moreover, we present various simulations that allow assessing the impact of belief revision on the negotiation process.

Quantitative Concession Behavior Analysis and Prediction for Decision Support in Electronic Negotiations

Quantitative analysis of negotiation concession behavior is performed based on empirical data with the purpose of providing simple and intuitive decision support in electronic negotiations. Previous work on non-linear concave preferences and subsequent concession crossover provides a theoretical basis for the model. The authors propose a model which quantifies the remaining concession potential for each issue and a generalization of the model which permits the memory/decay of past concessions. These models permit the analysis of negotiators' concession behavior. Using the proposed models, it was possible to quantitatively determine that negotiators in the authors' negotiation case exhibit concession crossover issues and thus have a tendency to give concessions on issues with the most remaining concession potential. This finding provides empirical evidence of concession crossover in actual concessions and the corresponding model permits the design of a simple and intuitive prediction methodology, which could be used in real world negotiations by decision support systems or automated negotiation agents.