scholarly journals On the realization of the recognition-primed decision model for artificial agents

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Syed Nasir Danial ◽  
Jennifer Smith ◽  
Brian Veitch ◽  
Faisal Khan
Keyword(s):  
Decision Making ◽  
Situation Awareness ◽  
Decision Model ◽  
Feature Matching ◽  
Human Mind ◽  
Mental Simulation ◽  
Main Concern ◽  
Human Beings ◽  
Naturalistic Decision Making ◽  
Markov Logic

Abstract This work proposes a methodology to program an artificial agent that can make decisions based on a naturalistic decision-making approach called recognition-primed decision model (RPDM). The proposed methodology represents the main constructs of RPDM in the language of Belief-Desire-Intention logic. RPDM considers decision-making as a synthesis of three phenomenal abilities of the human mind. The first is one’s use of experience to recognize a situation and suggest appropriate responses. The main concern here is on situation awareness because the decision-maker needs to establish that a current situation is the same or similar to one previously experienced, and the same solution is likely to work this time too. To this end, the proposed modeling approach uses a Markov logic network to develop an Experiential-Learning and Decision-Support module. The second component of RPDM deals with the cases when a decision-maker’s experience becomes secondary because the situation has not been recognized as typical. In this case, RPDM suggests a diagnostic mechanism that involves feature-matching, and, therefore, an ontology (of the domain of interest) based reasoning approach is proposed here to deal with all such cases. The third component of RPDM is the proposal that human beings use intuition and imagination (mental stimulation) to make sure whether a course of action should work in a given situation or not. Mental simulation is modeled here as a Bayesian network that computes the probability of occurrence of an effect when a cause is more likely. The agent-based model of RPDM has been validated with real (empirical) data to compare the simulated and empirical results and develop a correspondence in terms of the value of the result, as well as the reasoning.

scholarly journals AN EXPLORATORY RESEARCH ON THE PSYCHOLOGY OF INVESTORS IN FINANCIAL MARKETS

2020 ◽  
Vol 8 (4) ◽  
pp. 979-992
Author(s):  
Mustafa Hakan SALDI
Keyword(s):  
Decision Making ◽  
Financial Markets ◽  
Research Question ◽  
Endocrine System ◽  
Human Mind ◽  
Exploratory Research ◽  
Human Beings ◽  
Risky Assets ◽  
Main Research ◽  
The Relationship

The emotional mind which was granted to human beings in order to add the meaning of their perception through the data, information and knowledge that are being gathered from all around the outside environment with senses and the experiences of realities that have effects on the attitude of a person which can be observed as stereotypes, have effects on the decision making processes of investors, which was proven with general assumptions and theories with countless times in the background of the subject. Differently, this research is mainly designed for in-depth investigation of the relationship between parts of the human brain and endocrine system which have a role on emotional actions that can be observed of investors' behaviours in financial markets. From the viewpoint of experimentally tested studies, the discovery of the response of the subproblems will be explored in the main research question of why the risky assets are being selected by the investors relative to the sciences of neurology and endocrinology. Also, the amygdala, testosterone and cortisol relation which is the predictive factor of behaviours is going to be explained in terms of showing their effects on decision making in monetary management and will be analysed as a moderator with depth observations to understand the relationship between investment behaviour and emotions as well. As a result, the study will bring different perspectives to investors who are both experienced and inexperienced in trading with financial instruments by the addition of consideration of emotional side of the human mind to the logic and rational part.

Artificial Intelligence in International Arbitration

2020 ◽  
pp. 590-616
Author(s):  
Myriam Gicquello
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Rule Of Law ◽  
Human Beings ◽  
International Arbitration ◽  
Naturalistic Decision Making ◽  
The Rule Of Law ◽  
Behavioural Analysis ◽  
International Adjudication ◽  
Definition Of

This chapter assesses the introduction of artificial intelligence in international arbitration. The contention is that it would not only reinstate confidence in the arbitral system—from the perspective of the parties and the general public—and participate in the development of the rule of law, but also engage with broader systemic considerations in enhancing its legitimacy, fairness, and efficiency. Yet, before addressing the why, what, and how of this proposition, a definition of artificial intelligence is warranted. It should be noted at the outset that this concept has a variety of meanings. Despite the lack of consensus on its meaning, the chapter will thus treat artificial intelligence as encompassing both semi-autonomous and autonomous computer systems dedicated to assisting or replacing human beings in decision-making tasks. It presents the conclusions of two extensive research programs respectively dealing with the performance of statistical models and naturalistic decision-making. From that behavioural analysis, the introduction of artificial intelligence in international arbitration be discussed against the general considerations of international adjudication and the specific goals pertaining to international arbitration.

scholarly journals Intuitive Cognition

2017 ◽  
Vol 11 (1) ◽  
pp. 5-22 ◽  
Author(s):  
Robert Earl Patterson ◽  
Robert G. Eggleston
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Decision Model ◽  
Theoretical Foundation ◽  
Meaning Making ◽  
Naturalistic Decision Making ◽  
The Core ◽  
Situational Meaning ◽  
Extended Work ◽  
Everyday Living

In the naturalistic decision-making literature, intuitive cognition is at the heart of a pattern recognition–based decision model called the recognition-primed decision model. Given the importance of intuitive cognition in naturalistic decision-making theory, we explore the question of what makes intuitive cognition effective for decision making and, in so doing, present an extended empirical and theoretical foundation for the intuitive component in naturalistic decision making. We theorize that intuitive cognition is effective because it (1) possesses a capability for grounded, situational meaning making (sign interpretation); (2) is operative over extended work intervals involving interruptions; and (3) is instrumental in handling situated complexities of everyday living. Other characteristics of intuitive cognition and its foundations are discussed. We propose that intuitive cognition represents the core of cognition—grounded, situational meaning making—whereas analytical cognition represents a form of an intellectual exoskeleton that provides added capabilities (e.g., working memory).

Decision Making in the AEGIS Combat Information Center

1993 ◽  
Vol 37 (16) ◽  
pp. 1107-1111 ◽  
Author(s):  
George L. Kaempf ◽  
Steve Wolf ◽  
Thomas E. Miller
Keyword(s):  
Decision Making ◽  
Feature Matching ◽  
Time Pressure ◽  
Cognitive Strategies ◽  
Structured Interview ◽  
Naturalistic Decision Making ◽  
Interview Method ◽  
Course Of Action ◽  
Human Computer Interfaces ◽  
Information Center

This paper presents the methods and findings of a study designed to identify the decision requirements for anti-air warfare officers in the Combat Information Center of an AEGIS cruiser. Decision requirements include the decisions that systems operators make, the cognitive strategies they invoke to make these decisions, and the cues and factors essential for making these decisions. These requirements can be used to design training, human-computer interfaces, or decision supports. The researchers adopted a method based on Naturalistic Decision Making (NDM) research. NDM describes how people make decisions in real-world settings under conditions of time pressure, high risk, and ambiguity. This paper describes a process for obtaining data necessary for describing these decision processes. The central method is a semi-structured interview method, the Critical Decision method (CDM). CDM was used to interview 31 experienced AEGIS personnel resulting in 14 incidents that reflect real problems experienced by the operational fleet. Analysis of these incidents revealed 183 decisions. Of these, 103 concerned situation assessments (SA). The operators used feature matching and story building to make all SA decisions. The operators invoked recognitional strategies to generate 95% of the course of action (COA) options and compared multiple options in only 5% of the COA decisions. The findings reported here indicate that under conditions of time pressure and ambiguity: decision makers rarely use analytical decision strategies, they usually satisfice rather than optimize, they rely heavily on diagnostic decisions, and they invoke singular rather than comparative evaluations of courses of action.

Introduction to the Special Issue. Naturalistic Decision Making and Organizational Decision Making: Exploring the Intersections

2006 ◽  
Vol 27 (7) ◽  
pp. 917-923 ◽  
Author(s):  
Raanan Lipshitz ◽  
Gary Klein ◽  
John S. Carroll
Keyword(s):  
Decision Making ◽  
Situation Awareness ◽  
Decision Makers ◽  
Distributed Teams ◽  
Special Issue ◽  
Natural Habitats ◽  
Naturalistic Decision Making ◽  
Organizational Decision Making ◽  
Organizational Goals ◽  
Normative Economic

Although naturalistic decision making (NDM) and organizational decision making (ODM) have much in common, they hardly interact. Both NDM and ODM focus on what decision makers actually do in their ‘natural habitats’ and reject the equivalence of decision making with normative economic and statistical reasoning which can be studied in sparse laboratory settings. Linking with ODM would help NDM researchers to include organizational goals, norms, and other aspects of context in their models. Conversely, linking with NDM would provide ODM researchers with detailed descriptions of how individuals and groups perform functions such as decision making, sensemaking, and planning on the basis of pattern matching, story telling and argumentation, and detailed descriptions of the processes through which distributed teams build and maintain shared situation awareness. In the introduction to this special issue we outline the two fields, argue why they should be in closer contact, and summarize the papers contributed to this issue.

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