scholarly journals Inter-Individual and Inter-Strain Differences in Cognitive and Social Abilities of Dark Agouti and Wistar Han Rats

2019 ◽  
Author(s):  
Lucille Alonso ◽  
Polina Peeva ◽  
Arnau Ramos-Prats ◽  
Natalia Alenina ◽  
York Winter ◽  
...  
Keyword(s):  
Decision Making ◽  
Cognitive Abilities ◽  
Psychiatric Symptoms ◽  
Social Rank ◽  
Strain Differences ◽  
Dark Agouti ◽  
Good Decision ◽  
Individual Level ◽  
Behavioural Phenotypes ◽  
Different Strains

AbstractBackgroundHealthy animals showing extreme behaviours spontaneously that resemble human psychiatric symptoms are relevant models to study the natural psychobiological processes of maladapted behaviours. Healthy poor decision makers (PDMs) identified using a Rat Gambling Task, co-express a combination of cognitive and reward-based characteristics similar to symptoms observed in human patients with impulse-control disorders. The main goals of this study were to 1) confirm the existence of PDMs and their unique behavioural phenotypes in the Dark Agouti (DA) and Wistar Han (WH), 2) to extend the behavioural profile of the PDMs to probability-based decision-making and social behaviours and 3) to discuss how the key traits of each strain could be relevant for biomedical research.MethodsWe compared cognitive abilities, natural behaviours and physiological responses in DA and WH rats using several tests. We analysed the results at the strain and the individual level.ResultsPrevious findings in WH rats were reproduced and could be generalized to DA. Each PDM of either strain displayed a similar, naturally occurring, combination of behavioural traits, including possibly higher social rank, but no deficits in probability-based decision-making. A Random forest analysis revealed interesting discriminating traits between WH and DA.ConclusionThe reproducibility and conservation of the socio-cognitive and behavioural phenotypes of GDM (good decision maker) and PDM individuals in the two genetically different strains of WH and DA support a good translational validity of these phenotypes. Both DA and WH rat strains present large phenotypic variations in behaviour pertinent for the study of the underlying mechanisms of poor decision making and associated disorders.

scholarly journals The sovereignty of Artificial Intelligence over Human Ethics and Heedfulness

2021 ◽  
Vol 23 (08) ◽  
pp. 657-665
Author(s):  
Sunil Varma Mudundi ◽  
◽  
Tejaswi Pasumathy ◽  
Dr. Raul Villamarin Roudriguez ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Computer Vision ◽  
Critical Thinking ◽  
Cognitive Abilities ◽  
Machine Learning Algorithms ◽  
Good Decision ◽  
Deep Dive ◽  
Detection Techniques ◽  
Ethics And Technology

Artificial Intelligence in present days is in extreme growth. We see AI in almost every field in work today. Artificial Intelligence is being introduced in crucial roles like recruiting, Law enforcement and in the Military. To be involved in such crucial roles, it needs lots of trusts and scientific evaluation. With the evolution of artificial intelligence, automatic machines are in a speed run in this decade. Developing a machine/robot with a set of tools/programs will technically sort of some of the challenges. But the problem arises when we completely depend on robots/machines. Artificial intelligence this fast-growing technology will be very helpful when we take help from it for just primary needs like face detection, sensor-controllers, bill counters…etc. But we face real challenges when we involve with decision making, critical thinking…etc. In mere future, automated machines are going to replace many positions of humans. Many firms from small to big are opting for Autonomous means just to make their work simpler and efficient. Using a machine gives more accurate results and outputs in simulated time. As technology is developing fast, they should be developed as per societal rules and conditions. Scientists and analysts predict that singularity in AI can be achieved by 2047. Ray Kurzweil, Director of Technology at Google predicted that AI may achieve singularity in 2047. We all saw the DRDO invention on autonomous fighting drones. They operate without any human assistance. They evaluate target type, its features and eliminate them based on edge detection techniques using computer vision. AI is also into recruiting people for companies. Some companies started using AI Recruiter to evaluate the big pool of applications and select efficient ones into the industry. This is possible through computer vision and machine learning algorithms. In recent times AI is being used as a suggestion tool for judgement too. Apart from all these advancements, some malicious scenarios may affect humankind. When AI is used in the wrong way many lives will fall in danger. Collecting all good and evil from past experiences is it possible to feed a machine to work autonomously. As many philosophers and educated people kept some set of guidelines in society is it practically possible to follow when AI achieves singularity and when we talk about the neural networking of human. They have good decision-making skills, critical thinking…etc. We will briefly discuss the ethics and AI robots / Machines that involve consciousness and cognitive abilities. In this upgrading technological world, AI is ruling a maximum number of operations. So, we will discuss how can ethics be followed. How can we balance ethics and technology in both phases.We will deep dive into some of these interesting areas in this article.

scholarly journals Public health decisions in the COVID-19 pandemic require more than ‘follow the science’

2021 ◽  
pp. medethics-2020-107134
Author(s):  
Thana Cristina de Campos-Rudinsky ◽  
Eduardo Undurraga
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Health Policy ◽  
Empirical Evidence ◽  
Empirical Data ◽  
Public Health Policy ◽  
Scientific Evidence ◽  
Ethical Reasoning ◽  
Reliable Data ◽  
Good Decision

Although empirical evidence may provide a much desired sense of certainty amidst a pandemic characterised by uncertainty, the vast gamut of available COVID-19 data, including misinformation, has instead increased confusion and distrust in authorities’ decisions. One key lesson we have been gradually learning from the COVID-19 pandemic is that the availability of empirical data and scientific evidence alone do not automatically lead to good decisions. Good decision-making in public health policy, this paper argues, does depend on the availability of reliable data and rigorous analyses, but depends above all on sound ethical reasoning that ascribes value and normative judgement to empirical facts.

The value of sociality

foresight ◽  
2014 ◽  
Vol 16 (4) ◽  
pp. 309-328 ◽  
Author(s):  
Evgeniya Lukinova ◽  
Mikhail Myagkov ◽  
Pavel Shishkin
Keyword(s):  
Decision Making ◽  
Utility Function ◽  
Social Relationship ◽  
Social Utility ◽  
Content Type ◽  
Individual Level ◽  
Know How ◽  
General Utility ◽  
The Social ◽  
Individual Decisions

Purpose – This paper aims to study the value of sociality. Recent experimental evidence has brought to light that the assumptions of the Prospect Theory by Kahneman and Tversky do not hold in the proposed substantive domain of “sociality”. In particular, the desire to be a part of the social environment, i.e. the environment where individuals make decisions among their peers, is not contingent on the framing. Evolutionary psychologists suggest that humans are “social animals” for adaptive reasons. However, entering a social relationship is inherently risky. Therefore, it is extremely important to know how much people value “sociality”, when the social outcomes are valued more than material outcomes and what kinds of adaptations people use. Design/methodology/approach – We develop a new theory and propose the general utility function that features “sociality” component. We test the theory in the laboratory experiments carried out in several countries. Findings – Our results suggest that when stakes are low the theory of “sociality” is successful in predicting individual decisions: on average, people do value “sociality” and it surpasses the monetary loss. Originality/value – The main contribution of this paper is the breakdown of the risk attitudes under low stakes and individual level of decision-making. Another advancement is the ability to formalize the social utility or the theory of “sociality” in an economic model; we use general utility function that we define both on the outcomes and on the process of the decision-making itself and test in laboratory studies.

scholarly journals Cooperation and the evolution of intelligence

2012 ◽  
Vol 279 (1740) ◽  
pp. 3027-3034 ◽  
Author(s):  
Luke McNally ◽  
Sam P. Brown ◽  
Andrew L. Jackson
Keyword(s):  
Decision Making ◽  
Social Interactions ◽  
Cognitive Abilities ◽  
Artificial Neural Network Model ◽  
Social Intelligence ◽  
Arms Race ◽  
Selection Pressures ◽  
Social Intelligence Hypothesis ◽  
The Social ◽  
Selection For

The high levels of intelligence seen in humans, other primates, certain cetaceans and birds remain a major puzzle for evolutionary biologists, anthropologists and psychologists. It has long been held that social interactions provide the selection pressures necessary for the evolution of advanced cognitive abilities (the ‘social intelligence hypothesis’), and in recent years decision-making in the context of cooperative social interactions has been conjectured to be of particular importance. Here we use an artificial neural network model to show that selection for efficient decision-making in cooperative dilemmas can give rise to selection pressures for greater cognitive abilities, and that intelligent strategies can themselves select for greater intelligence, leading to a Machiavellian arms race. Our results provide mechanistic support for the social intelligence hypothesis, highlight the potential importance of cooperative behaviour in the evolution of intelligence and may help us to explain the distribution of cooperation with intelligence across taxa.

Novel Application of Artificial Intelligence with Potential to Transform Well Planning Workflows on the Norwegian Continental Shelf

2021 ◽  
Author(s):  
Jon Gustav Vabø ◽  
Evan Thomas Delaney ◽  
Tom Savel ◽  
Norbert Dolle
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Continental Shelf ◽  
Data Science ◽  
Search Algorithm ◽  
Solution Space ◽  
Good Decision ◽  
Trade Offs ◽  
Well Trajectory ◽  
Norwegian Continental Shelf

Abstract This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk. Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft. The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks "wide" in the option space). The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives. The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning. Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows. There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.

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