scholarly journals A Model of Spontaneous Remission From Addiction

2019 ◽  
Vol 8 (1) ◽  
pp. 21-48
Author(s):  
Chiara Mocenni ◽  
Giuseppe Montefrancesco ◽  
Silvia Tiezzi
Keyword(s):  
Decision Making ◽  
Formal Model ◽  
Spontaneous Remission ◽  
Environmental Cues ◽  
Individual Decision ◽  
Individual Decision Making ◽  
Self Evaluation ◽  
Consumption Choices ◽  
Drug And Alcohol

This article develops a formal model of spontaneous recovery from pathological addiction. It regards addiction as a progressive susceptibility to stochastic environmental cues and introduce a cognitive appraisal process in individual decision making depending on past addiction experiences and on their future expected consequences. This process affects consumption choices in two ways. The reward from use decreases with age. At the same time, cognitive incentives emerge that reduce the probability of making mistakes. In addition to modeling the role of cue-triggered mistakes in individual decision making, the analysis highlights the role of other factors such as subjective self-evaluation and cognitive control. The implications for social policy and for the treatment of drug and alcohol dependence are discussed.

scholarly journals Deciding on breast cancer risk reduction: The role of counseling in individual decision-making – A qualitative study

2017 ◽  
Vol 100 (12) ◽  
pp. 2346-2354 ◽  
Author(s):  
Sarah B. Blakeslee ◽  
Worta McCaskill-Stevens ◽  
Patricia A. Parker ◽  
Christine M. Gunn ◽  
Hanna Bandos ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Breast Cancer Risk ◽  
Qualitative Study ◽  
Cancer Risk ◽  
Individual Decision ◽  
Individual Decision Making ◽  
Breast Cancer Risk Reduction ◽  
Cancer Risk Reduction

scholarly journals Artificial intelligence in educational leadership: a symbiotic role of human-artificial intelligence decision-making

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yinying Wang
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Educational Leadership ◽  
Data Driven ◽  
Individual Decision ◽  
Moral Decision Making ◽  
Organizational Decision Making ◽  
Individual Decision Making ◽  
Content Type

PurposeArtificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision-making. This position paper looks beyond the sensational hyperbole of AI in teaching and learning. Instead, this paper aims to explore the role of AI in educational leadership.Design/methodology/approachTo explore the role of AI in educational leadership, I synthesized the literature that intersects AI, decision-making, and educational leadership from multiple disciplines such as computer science, educational leadership, administrative science, judgment and decision-making and neuroscience. Grounded in the intellectual interrelationships between AI and educational leadership since the 1950s, this paper starts with conceptualizing decision-making, including both individual decision-making and organizational decision-making, as the foundation of educational leadership. Next, I elaborated on the symbiotic role of human-AI decision-making.FindingsWith its efficiency in collecting, processing, analyzing data and providing real-time or near real-time results, AI can bring in analytical efficiency to assist educational leaders in making data-driven, evidence-informed decisions. However, AI-assisted data-driven decision-making may run against value-based moral decision-making. Taken together, both leaders' individual decision-making and organizational decision-making are best handled by using a blend of data-driven, evidence-informed decision-making and value-based moral decision-making. AI can function as an extended brain in making data-driven, evidence-informed decisions. The shortcomings of AI-assisted data-driven decision-making can be overcome by human judgment guided by moral values.Practical implicationsThe paper concludes with two recommendations for educational leadership practitioners' decision-making and future scholarly inquiry: keeping a watchful eye on biases and minding ethically-compromised decisions.Originality/valueThis paper brings together two fields of educational leadership and AI that have been growing up together since the 1950s and mostly growing apart till the late 2010s. To explore the role of AI in educational leadership, this paper starts with the foundation of leadership—decision-making, both leaders' individual decisions and collective organizational decisions. The paper then synthesizes the literature that intersects AI, decision-making and educational leadership from multiple disciplines to delineate the role of AI in educational leadership.

Minimizing Missed Opportunities: Modeling Relationships between the State and Business in Russia

2009 ◽  
pp. 42-61
Author(s):  
A. Oleynik
Keyword(s):  
Decision Making ◽  
Empirical Evidence ◽  
Common Good ◽  
The State ◽  
Power Relationships ◽  
Individual Decision ◽  
Missed Opportunities ◽  
Power Domination ◽  
Individual Decision Making ◽  
Optimal Outcomes

Power involves a number of models of choice: maximizing, satisficing, coercion, and minimizing missed opportunities. The latter is explored in detail and linked to a particular type of power, domination by virtue of a constellation of interests. It is shown that domination by virtue of a constellation of interests calls for justification through references to a common good, i.e. a rent to be shared between Principal and Agent. Two sources of sub-optimal outcomes are compared: individual decision-making and interactions. Interactions organized in the form of power relationships lead to sub-optimal outcomes for at least one side, Agent. Some empirical evidence from Russia is provided for illustrative purposes.

scholarly journals Humans rely more on algorithms than social influence as a task becomes more difficult

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Bogert ◽  
Aaron Schecter ◽  
Richard T. Watson
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Social Influence ◽  
Correct Answer ◽  
Online Reviews ◽  
Personal Networks ◽  
Human Judgment ◽  
Individual Decision ◽  
Individual Decision Making

AbstractAlgorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three experiments focused on an intellective task with a correct answer and found that subjects relied more on algorithmic advice as difficulty increased. This effect persisted even after controlling for the quality of the advice, the numeracy and accuracy of the subjects, and whether subjects were exposed to only one source of advice, or both sources. Subjects also tended to more strongly disregard inaccurate advice labeled as algorithmic compared to equally inaccurate advice labeled as coming from a crowd of peers.

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