Regrets Associated with Providing Healthcare: Qualitative Study of Experiences of Hospital-Based Physicians and Nurses

Regret is an unavoidable corollary of clinical practice. Physicians and nurses perform countless clinical decisions and actions, in a context characterised by time pressure, information overload, complexity and uncertainty

Slower is not always better: Response-time evidence clarifies the limited role of miserly information processing in the Cognitive Reflection Test

We report a study examining the role of `cognitive miserliness' as a determinant of poor performance on the standard three-item Cognitive Reflection Test (CRT). The cognitive miserliness hypothesis proposes that people often respond incorrectly on CRT items because of an unwillingness to go beyond default, heuristic processing and invest time and effort in analytic, reflective processing. Our analysis (N = 391) focused on people's response times to CRT items to determine whether predicted associations are evident between miserly thinking and the generation of incorrect, intuitive answers. Evidence indicated only a weak correlation between CRT response times and accuracy. Item-level analyses also failed to demonstrate predicted response time differences between correct analytic and incorrect intuitive answers for two of the three CRT items. We question whether participants who give incorrect intuitive answers on the CRT can legitimately be termed cognitive misers and whether the three CRT items measure the same general construct

Short-term reward experience biases inference despite dissociable neural correlates

Contains fulltext : 179643.pdf (publisher's version ) (Open Access)Optimal decision-making employs short-term rewards and abstract long-term information based on which of these is deemed relevant. Employing short- vs. long-term information is associated with different learning mechanisms, yet neural evidence showing that these two are dissociable is lacking. Here we demonstrate that long-term, inference-based beliefs are biased by short-term reward experiences and that dissociable brain regions facilitate both types of learning. Long-term inferences are associated with dorsal striatal and frontopolar cortex activity, while short-term rewards engage the ventral striatum. Stronger concurrent representation of reward signals by mediodorsal striatum and frontopolar cortex correlates with less biased, more optimal individual long-term inference. Moreover, dynamic modulation of activity in a cortical cognitive control network and the medial striatum is associated with trial-by-trial control of biases in belief updating. This suggests that counteracting the processing of optimally to-be-ignored short-term rewards and cortical suppression of associated reward-signals, determines long-term learning success and failure.14 p

Stock price formation: useful insights from a multi-agent reinforcement learning model

arXiv admin note: text overlap with arXiv:1909.07748In the past, financial stock markets have been studied with previous generations of multi-agent systems (MAS) that relied on zero-intelligence agents, and often the necessity to implement so-called noise traders to sub-optimally emulate price formation processes. However recent advances in the fields of neuroscience and machine learning have overall brought the possibility for new tools to the bottom-up statistical inference of complex systems. Most importantly, such tools allows for studying new fields, such as agent learning, which in finance is central to information and stock price estimation. We present here the results of a new generation MAS stock market simulator, where each agent autonomously learns to do price forecasting and stock trading via model-free reinforcement learning, and where the collective behaviour of all agents decisions to trade feed a centralised double-auction limit order book, emulating price and volume microstructures. We study here what such agents learn in detail, and how heterogenous are the policies they develop over time. We also show how the agents learning rates, and their propensity to be chartist or fundamentalist impacts the overall market stability and agent individual performance. We conclude with a study on the impact of agent information via random trading

Mesoscale impact of trader psychology on stock markets: a multi-agent AI approach

9 pages, 15 figuresRecent advances in the fields of machine learning and neurofinance have yielded new exciting research perspectives in practical inference of behavioural economy in financial markets and microstructure study. We here present the latest results from a recently published stock market simulator built around a multi-agent system architecture, in which each agent is an autonomous investor trading stocks by reinforcement learning (RL) via a centralised double-auction limit order book. The RL framework allows for the implementation of specific behavioural and cognitive traits known to trader psychology, and thus to study the impact of these traits on the whole stock market at the mesoscale. More precisely, we narrowed our agent design to three such psychological biases known to have a direct correspondence with RL theory, namely delay discounting, greed, and fear. We compared ensuing simulated data to real stock market data over the past decade or so, and find that market stability benefits from larger populations of agents prone to delay discounting and most astonishingly, to greed

Behavioural and neural characterization of optimistic reinforcement learning

When forming and updating beliefs about future life outcomes, people tend to consider good news and to disregard bad news. This tendency is assumed to support the optimism bias. Whether this learning bias is specific to ‘high-level’ abstract belief update or a particular expression of a more general ‘low-level’ reinforcement learning process is unknown. Here we report evidence in favour of the second hypothesis. In a simple instrumental learning task, participants incorporated better-than-expected outcomes at a higher rate than worse-than-expected ones. In addition, functional imaging indicated that inter-individual difference in the expression of optimistic update corresponds to enhanced prediction error signalling in the reward circuitry. Our results constitute a step towards the understanding of the genesis of optimism bias at the neurocomputational level