Human Judgment and AI Pricing

2018 ◽  
Vol 108 ◽  
pp. 58-63
Author(s):  
Ajay Agrawal ◽  
Joshua S. Gans ◽  
Avi Goldfarb
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Maker ◽  
Decision Makers ◽  
The Other ◽  
Human Judgment

This paper examines the pricing choices of a provider of artificial intelligence (AI) services. It does so in the context of AI providing predictions to a decision-maker who also exercises what we term judgment; specifically, the discovery of payoffs from action/state pairs. An AI facilitates the decision-maker obtaining judgment through experience, which is one source of demand for AI services. The other source is prediction when (and if) the decision-maker has a need for state-contingent decision-making. We show that the need to encourage learning means that the AI provider is constrained in its ability to extract rents from decision-makers.

Property Rights within Government and Devices to Increase Government Efficiency

1976 ◽  
Vol 4 (2) ◽  
pp. 151-158
Author(s):  
Ryan C. Amacher ◽  
Robert D. Tollison
Keyword(s):  
Decision Making ◽  
Property Rights ◽  
Decision Maker ◽  
Decision Makers ◽  
The Other ◽  
Complex Process ◽  
Government Efficiency ◽  
Gains And Losses

This paper demonstrates that bureaucratic decision-making is a more complex process than the literature that focuses narrowly on the lack of appropriability of gains and losses from efficient decision-making implies. The paper delineates some of the other types of constraints under which the governmental decision maker operates. These factors lead to the conclusion that there are many devices (like the volunteer army) that can move decision makers toward significantly more efficient decisions without the presence of appropriability (narrowly defined).

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals Studying the prominence effect amid the COVID-19 crisis: implications for public health policy decision-making.

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 1356
Author(s):  
Yossi Maaravi ◽  
Ben Heller
Keyword(s):  
Decision Making ◽  
Policy Decision ◽  
Matching Condition ◽  
Decision Makers ◽  
The Other ◽  
Choice Condition ◽  
Health Concerns ◽  
Prominence Effect ◽  
Novel Coronavirus ◽  
The Cost

The novel coronavirus disease 2019 (COVID-19) has brought with it crucial policy- and decision-making situations, especially when making judgments between financial and health concerns. One particularly relevant decision-making phenomenon is the prominence effect, where decision-makers base their decisions on the most prominent attribute of the object at hand (e.g., health concerns) rather than weigh all the attributes together. This bias diminishes when the decision-making mode inhibits heuristic processes. In this study, we tested the prominence of health vs. financial concerns across two decision-making modes - choice (prone to heuristics) and matching (mitigates heuristics) - during the peak of the COVID-19 in the UK using Tversky et al.’s classic experimental paradigm. We added to the classic experimental design a priming condition. Participants were presented with two casualty-minimization programs, differing in lives saved and costs: program X would save 100 lives at the cost of 55-million-pound sterling, whereas program Y would save 30 lives at the cost of 12-million-pound sterling. Half of the participants were required to choose between the programs (choice condition). The other half were not given the cost of program X and were asked to determine what the cost should be to make it as equally attractive as the program Y. Participants in both groups were primed for either: a) financial concerns; b) health concerns; or c) control (no priming). Results showed that in the choice condition, unless primed for financial concerns, health concerns are more prominent. In the matching condition, on the other hand, the prominence of health concerns did not affect decision-makers, as they all “preferred” the cheaper option. These results add further support to the practical relevance of using the proper decision-making modes in times of consequential crises where multiple concerns, interests, and parties are involved.

scholarly journals Stochastic Fuzzy Algorithms for Impairment of Assets Management

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Marcel Ioan Bolos ◽  
Victoria Bogdan ◽  
Ioana Alexandra Bradea ◽  
Claudia Diana Sabau Popa ◽  
Dorina Nicoleta Popa
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Fuzzy Numbers ◽  
Cash Flows ◽  
The Other ◽  
Dual Purpose ◽  
Fuzzy Algorithms ◽  
Tangible Assets ◽  
The Impact

The present paper aims to analyze the impairment of tangible assets with the help of artificial intelligence. Stochastic fuzzy numbers have been introduced with a dual purpose: on one hand to estimate the cash flows generated by tangible assets exploitation and, on the other hand, to ensure the value ranges stratifications that define these cash flows. Estimation of cash flows using stochastic fuzzy numbers was based on cash flows generated by tangible assets in previous periods of operation. Also, based on the Lagrange multipliers, were introduced: the objective function of minimizing the standard deviations from the recorded value of the cash flows generated by the tangible assets, as well as the constraints caused by the impairment of tangible assets identification according to which the cash flows values must be equal to the annual value of the invested capital. Within the determination of the impairment value and stratification of the value ranges determined by the cash flows using stochastic fuzzy numbers, the impairment of assets risk was identified. Information provided by impairment of assets but also the impairment risks, is the basis of the decision-making measures taken to mitigate the impact of accumulated impairment losses on company’s financial performance.

scholarly journals Applicability of Artificial Intelligence in Decision-Making for Land Forces

2021 ◽  
Vol 30 (2) ◽  
pp. 039-054
Author(s):  
Paul Tudorache
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Research Methodology ◽  
Research Paper ◽  
The Other ◽  
Military Operations ◽  
Other Hand ◽  
The Military

Similar to other fields, also in the military one, the Artificial Intelligence has become recently an evident solution for optimizing specific processes and activities. Therefore, this research paper aims to highlight the potential uses of Artificial Intelligence in the military operations carried out by the Land Forces. In this regard, analysing the framework of the operations process and applying suitable research methodology, the main findings are related to AI’s contributions in optimizing commander’s decisions during the progress of planning and execution. On the other hand, picturing the AI upgrated combat power of the Land Forces is another significant result of this study.

Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence

2021 ◽  
Author(s):  
Ekaterina Jussupow ◽  
Kai Spohrer ◽  
Armin Heinzl ◽  
Joshua Gawlitza
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Errors ◽  
Decision Makers ◽  
Medical Decision ◽  
Decision Making Process ◽  
Self Monitoring ◽  
Rule Based ◽  
Human Actors

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors.

Artificial Intelligence for Decision Makers

2019 ◽  
pp. 0000-0000
Author(s):  
Viktor Elliot ◽  
Mari Paananen ◽  
Miroslaw Staron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Accounting Data ◽  
Ethical Concerns ◽  
Key Concepts ◽  
Basic Understanding ◽  
Visualization Tools

We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns.

Material Selection Using a Novel Multiple Attribute Decision Making Method

2012 ◽  
pp. 252-266
Author(s):  
R. V. Rao ◽  
B. K. Patel
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Decision Maker ◽  
Design Process ◽  
Material Selection ◽  
Multiple Attribute Decision Making ◽  
Decision Makers ◽  
Selection Problem ◽  
Multiple Attribute ◽  
Selection Of

Selection of a most appropriate material is a very important task in design process of every product. There is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelations and in making right decisions. This paper proposes a novel multiple attribute decision making (MADM) method for solving the material selection problem. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. Two examples are presented to illustrate the potential of the proposed method.

Institutional Structures

2021 ◽  
pp. 32-64
Author(s):  
Paul Daly
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Judicial Review ◽  
Decision Makers ◽  
Administrative Action ◽  
Institutional Structures ◽  
General Aspects ◽  
Individual Self ◽  
Personal Interests ◽  
Or History

This chapter is concerned with the structure of administrative decision-making institutions. Two general aspects of this important topic are particularly relevant to the law of judicial review of administrative action. First, the no-bias principle ensures that decision-making is impartial, by preventing decision-makers from acting where their personal interests, conduct or history could conceivably raise a concern about their ability to make a dispassionate decision on the merits. Second, the principle that a decision-maker must retain their discretion prevents decision-makers from delegating their powers (subject to an exception in the case of government ministers) and limits the scope for the development of policies about how discretionary powers will be exercised in the future. These principles can be understood as being structured by the values of individual self-realisation, good administration, electoral legitimacy and decisional autonomy.

Artificial Intelligence, Human Intelligence, and Orientism Management Framework

2021 ◽  
pp. 1-30
Author(s):  
Luisa Dall'Acqua
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Makers ◽  
Human Intelligence ◽  
Decision Making Process ◽  
Knowledge Process ◽  
Management Framework ◽  
Theoretical Contribution ◽  
Practical Guideline

The chapter intends to be a theoretical contribution for developers in the field of artificial intelligence. It also means a practical guideline for leaders, as decision-makers, to manage tasks and optimize performance. The proposed approach interprets the fluid nature of the decision-making process looking at knowledge and knowledge activities as dynamic, adaptive, and self-regulative, based not only on well-known explicit curricular goals but also on unpredictable interactions and relationships between players. The knowledge process is emerging in human and biological, social, and cultural environments.

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