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Negotiation is a dialogue between two or more parties to resolve points of difference, gain an advantage for an individual or collective, or craft outcomes to satisfy various interests. The parties aspire to agree on matters of mutual interest. The agreement can be beneficial for all or some of the parties involved. The negotiators should establish their own needs and wants while also seeking to understand the wants and needs of others involved to increase their chances of closing deals, avoiding conflicts, forming relationships with other parties, or maximizing mutual gains. Distributive negotiations, or compromises, are conducted by putting forward a position and making concessions to achieve an agreement. The degree to which the negotiating parties trust each other to implement the negotiated solution is a major factor in determining the success of a negotiation.
Bounded rationality is the idea that rationality is limited when individuals make decisions, and under these limitations, rational individuals will select a decision that is satisfactory rather than optimal.
In psychology, decision-making is regarded as the cognitive process resulting in the selection of a belief or a course of action among several possible alternative options. It could be either rational or irrational. The decision-making process is a reasoning process based on assumptions of values, preferences and beliefs of the decision-maker. Every decision-making process produces a final choice, which may or may not prompt action.
Natural language generation (NLG) is a software process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems than can produce understandable texts in English or other human languages from some underlying non-linguistic representation of information".
The anchoring effect is a psychological phenomenon in which an individual's judgements or decisions are influenced by a reference point or "anchor" which can be completely irrelevant. Both numeric and non-numeric anchoring have been reported in research. In numeric anchoring, once the value of the anchor is set, subsequent arguments, estimates, etc. made by an individual may change from what they would have otherwise been without the anchor. For example, an individual may be more likely to purchase a car if it is placed alongside a more expensive model. Prices discussed in negotiations that are lower than the anchor may seem reasonable, perhaps even cheap to the buyer, even if said prices are still relatively higher than the actual market value of the car. Another example may be when estimating the orbit of Mars, one might start with the Earth's orbit and then adjust upward until they reach a value that seems reasonable.
A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
In intelligence and artificial intelligence, an intelligent agent (IA) is an agent acting in an intelligent manner; It perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostat or other control system is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state, or a biome.
Privacy-enhancing technologies (PET) are technologies that embody fundamental data protection principles by minimizing personal data use, maximizing data security, and empowering individuals. PETs allow online users to protect the privacy of their personally identifiable information (PII), which is often provided to and handled by services or applications. PETs use techniques to minimize an information system's possession of personal data without losing functionality. Generally speaking, PETs can be categorized as either hard or soft privacy technologies.
An intelligent decision support system (IDSS) is a decision support system that makes extensive use of artificial intelligence (AI) techniques. Use of AI techniques in management information systems has a long history – indeed terms such as "Knowledge-based systems" (KBS) and "intelligent systems" have been used since the early 1980s to describe components of management systems, but the term "Intelligent decision support system" is thought to originate with Clyde Holsapple and Andrew Whinston in the late 1970s. Examples of specialized intelligent decision support systems include Flexible manufacturing systems (FMS), intelligent marketing decision support systems and medical diagnosis systems.
Sarit Kraus is a professor of computer science at the Bar-Ilan University in Israel. She was named the 2020-2021 ACM Athena Lecturer recognising her contributions to artificial intelligence, notably to multiagent systems, human-agent interaction, autonomous agents and nonmonotonic reasoning, in addition to exemplary service and leadership in these fields.
Intra-household bargaining refers to negotiations that occur between members of a household in order to arrive at decisions regarding the household unit, like whether to spend or save or whether to study or work.
Automation bias is the propensity for humans to favor suggestions from automated decision-making systems and to ignore contradictory information made without automation, even if it is correct. Automation bias stems from the social psychology literature that found a bias in human-human interaction that showed that people assign more positive evaluations to decisions made by humans than to a neutral object. The same type of positivity bias has been found for human-automation interaction, where the automated decisions are rated more positively than neutral. This has become a growing problem for decision making as intensive care units, nuclear power plants, and aircraft cockpits have increasingly integrated computerized system monitors and decision aids to mostly factor out possible human error. Errors of automation bias tend to occur when decision-making is dependent on computers or other automated aids and the human is in an observatory role but able to make decisions. Examples of automation bias range from urgent matters like flying a plane on automatic pilot to such mundane matters as the use of spell-checking programs.
ACM SIGAI is the Association for Computing Machinery's Special Interest Group on Artificial Intelligence (AI), an interdisciplinary group of academic and industrial researchers, practitioners, software developers, end users, and students who work together to promote and support the growth and application of AI principles and techniques throughout computing. SIGAI is one of the oldest special interest groups in the ACM. SIGAI, previously called SIGART, started in 1966, publishing the SIGART Newsletter that later became the SIGART Bulletin and Intelligence Magazine.
Problem structuring methods (PSMs) are a group of techniques used to model or to map the nature or structure of a situation or state of affairs that some people want to change. PSMs are usually used by a group of people in collaboration to create a consensus about, or at least to facilitate negotiations about, what needs to change. Some widely adopted PSMs include
Argument technology is a sub-field of collective intelligence and artificial intelligence that focuses on applying computational techniques to the creation, identification, analysis, navigation, evaluation and visualisation of arguments and debates.
Sequential bargaining is a structured form of bargaining between two participants, in which the participants take turns in making offers. Initially, person #1 has the right to make an offer to person #2. If person #2 accepts the offer, then an agreement is reached and the process ends. If person #2 rejects the offer, then the participants switch turns, and now it is the turn of person #2 to make an offer. The people keep switching turns until either an agreement is reached, or the process ends with a disagreement due to a certain end condition. Several end conditions are common, for example:
A multi-attribute auction is a type of auction in which the bids have multiple parts. Multi-attribute auctions allow agents to sell and purchase goods and services, taking into account more attributes than just price.
Algorithm aversion is "biased assessment of an algorithm which manifests in negative behaviours and attitudes towards the algorithm compared to a human agent." It describes a phenomenon where humans reject advice from an algorithm in a case where they would accept the same advice if they thought it was coming from another human.
Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.
References
- ↑ Faratin, P.; Sierra, C.; Jennings, N.R. (December 2002). "Using similarity criteria to make issue trade-offs in automated negotiations". Artificial Intelligence. 142 (2): 205–237. doi:10.1016/S0004-3702(02)00290-4. hdl: 10261/162977 .
- ↑ Jennings, N.R.; Faratin, P.; Lomuscio, A.R.; Parsons, S.; Wooldridge, M.J.; Sierra, C. (2001). "Automated negotiation: prospects, methods and challenges". Group Decision and Negotiation. 10 (2): 199–215. doi:10.1023/A:1008746126376. S2CID 797384.
- ↑ Kraus, Sarit (2001). "Automated negotiation and decision making in multiagent environments". Mutli-agents Systems and Applications. Springer-Verlag New York, Inc. pp. 150–172. ISBN 9783540423126.
- ↑ Kersten, Gregory E.; Lai, Hsiangchu (10 October 2007). "Negotiation Support and E-negotiation Systems: An Overview". Group Decision and Negotiation. 16 (6): 553–586. doi:10.1007/s10726-007-9095-5. S2CID 1634324.
- ↑ Lin, Raz; Kraus, Sarit (1 January 2010). "Can automated agents proficiently negotiate with humans?". Communications of the ACM. 53 (1): 78. doi:10.1145/1629175.1629199.
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