Out of the 188 cognitive biases that exist, there is a much narrower group of biases that has a disproportionately large effect on the ways we do business. These are things that affect workplace culture, budget estimates, deal outcomes, and our perceived return on investments within the company. Mental mistakes such as these can add up quickly, and can hamper any organization in reaching its full bottom line potential. Today's infographic from Raconteur aptly highlights 18 different cognitive bias examples that can create particularly difficult challenges for company decision-making. Financial biases These are imprecise mental shortcuts we make with numbers, such as hyperbolic discounting – the mistake of preferring a smaller, sooner payoff instead of a larger, later reward.

Humans by their nature have many cognitive biases. This can become detrimental to real scientific progress. Research tends to be bias in favor of approaches that many experts have invested countless years of study. The consequence of this is that we ignore many intrinsic characteristics found in the very system under study. Thus researchers can unfortunately consume a lifetime pursuing a wrong and pointless path. History is littered with research that in hindsight were discovered to be incorrect and therefore worthless.

Companies from a wide range of industries use machine learning data to do everyday business. From consumer marketing and workforce management to healthcare treatment decision solutions and public safety and policing solutions, whether you realize it or not your life is increasingly more affected by the outcomes of machine learning algorithms. Machine learning algorithms make decisions like who gets a bonus, a job interview, whether or not your credit card limit (or interest) is raised, and who gets into a clinical trial. Machine learning algorithms even help make decisions about who gets parole and who languishes in prison. The result is that people's lives and livelihood are affected by the decisions made by machines.

Most people, at this point, believe that climate change is a real thing that will harm future generations of humans. And yet, a cognitive dissonance exists around that knowledge and our sense of responsibility: A much smaller percentage of people believe that climate change is impacting them personally, according to Yale's climate survey program. It is indeed impacting humans right now, with clear and compelling evidence that the global average temperature is much higher than anything modern society has experienced. And that has lead us to a whole host of issues, some of which WIRED writer Adam Rogers discusses with the Gadget Lab team on this week's podcast. So what can we humans do to fix things – and how much of it can actually be fixed by personal actions, versus widespread policy?

In this paper, we make a review on the concepts of rationality across several different fields, namely in economics, psychology and evolutionary biology and behavioural ecology. We review how processes like natural selection can help us understand the evolution of cognition and how cognitive biases might be a consequence of this natural selection. In the end we argue that humans are not irrational, but rather rationally bounded and we complement the discussion on how quantum cognitive models can contribute for the modelling and prediction of human paradoxical decisions.

My research aims to contribute to research in the narrative authoring domain by using cognitive models in narrative plan generation. These cognitive models determine how actions and events in narrative affect the audience. My research intends to leverage these models in narrative planning and use them to provide intelligent narrative plans that are structured to invoke specific responses from audiences when they experience the narrative. This sort of approach would greatly benefit the enrich growing set of variables of narrative planning.

In this paper we present two studies supporting a plan-based model of narrative generation that reasons about both intentionality and belief. First we compare the believability of agent plans taken from the spaces of valid classical plans, intentional plans, and belief plans. We show that the plans that make the most sense to humans are those in the overlapping regions of the intentionality and belief spaces. Second, we validate the model’s approach to representing anticipation, where characters form plans that involve actions they expect other characters to take. Using a short interactive scenario we demonstrate that players not only find it believable when NPCs anticipate their actions, but sometimes actively anticipate the actions of NPCs in a way that is consistent with the model.

Daniel Kahneman was awarded the Nobel Prize in Economic Science in 2002 for his pioneering studies on the identification and estimation of the psychological factors that influence human behaviour under uncertainty, which led to the birth of a new domain called behavioural economics. Cognitive psychologists have assumed for years, often implicitly, that complex cognitive processes, like human judgement and decision-making (DM), have to be modelled by combining set-theoretic structures and should obey to mathematical relations that resemble those typically used in logic, formalized by Boole (Boolean logic), and probability, axiomatized by Kolmogorov (Kolmogorovian probability) [1]. These structures are known in physics as classical structures: they were originally used in classical physics, and later extended to statistics, psychology, economics, finance and computer science. Classical structures are also implicitly assumed in the so-called Bayesian approach, according to which any source of uncertainty can be formalized probabilistically, while people update knowledge according to the Bayes law of Kolmogorovian probability. Finally, classical structures are the building blocks of subjective expected utility theory (SEUT), providing both the descriptive and the normative foundations of rational DM: in situations of uncertainty, people (should) choose as if they maximized EU with respect to a unique probability measure, satisfying the axioms of Kolmogorov and interpreted as their subjective probability [2, 3]. However, on the one side, empirical research in cognitive psychology has revealed that classical structures are not generally able to model human judgements and decisions, thus making problematical the 1 interpretation of a wide range of cognitive phenomena in terms of standard logic and probability theory. On the other side, Kahneman, Tversky and other authors suggested that these empirical deviations from classicality are "true errors" of human reasoning, whence the use of terms like "effect", "fallacy", "paradox", "contradiction", etc., to refer to such phenomena [4, 5].

Karthik Sundaram, Program Manager-The Industrial Internet of Things, Frost & Sullivan – an excerpt from SPS IPC Drives 2018 presentation to be delivered 28th of November 2018 at 2.00-2.30 Situated in a mountain village of Japan is FANUC's widely reported lights out factory. This one of a kind, unmanned factory works autonomously 24/7 and is well known for robots that can assemble, test, and monitor themselves. A few decades ago, such a scenario would have existed only in the pages of Isaac Asimov's science fiction. Today, the FANUC use case is a proof of the dawn of cognitive factories and how far artificial intelligence (AI) has been able to penetrate into the walls of these factories.

In spite of the fact that the idea of Artificial Intelligence has been around for a long time, it is just in the most recent years that it has gotten on the tech charts and is trending in each and every industry conceivable. Getting to be noticeably extraordinary compared to other cherished techs among the ingenious minds all over the world, Artificial Intelligence demands a mix of computer science, mathematics, cognitive psychology, and engineering. There is no doubt about that soon the demand for experts prepared in Artificial Intelligence would beat supply. In spite of the fact that there is some overlap of Artificial Intelligence with analytics, a capable Artificial Intelligence expert would have profound knowledge on spheres like computer vision, natural language processing, robotics automation, and machine learning. Artificial Intelligence education is still in its youthful days.