A reinforcement learning theory for homeostatic regulation

Reinforcement learning models address animal’s behavioral adaptation to its changing “external” environment, and are based on the assumption that Pavlovian, habitual and goal-directed responses seek to maximize reward acquisition. Negative-feedback models of homeostatic regulation, on the other hand, are concerned with behavioral adaptation in response to the “internal” state of the animal, and assume that animals’ behavioral objective is to minimize deviations of some key physiological variables from their hypothetical setpoints. Building upon the drive-reduction theory of reward, we propose a new analytical framework that integrates learning and regulatory systems, such that the two seemingly unrelated objectives of reward maximization and physiological-stability prove to be identi- cal. The proposed theory shows behavioral adaptation to both internal and external states in a disciplined way. We further show that the proposed framework allows for a unified explanation of some behavioral pattern like motivational sensitivity of different associative learning mechanism, anticipatory responses, interaction among competing motivational systems, and risk aversion

Neuroscience Can Help Us Understand Social Transitions

Human cultural adaptability helped our species get through several extreme environmental crises during the 200,000 year history of Homo sapiens. Richerson, Boyd and Henrich (2010) argue that this adaptability is a product of gene-culture coevolution. Much has been written about cultural evolution, but relatively little attention has been paid to the role human neurobiology plays in this process. I argue here that neuroscience can make important contributions to understanding human behavior within highly evolved social systems. This can help inform us as to how a transition to sustainability might be possible as we struggle to make it through the population, climate change, and resource bottlenecks of the 21st century. I argue further than the idea of homeostasis can serve as an organizing principle to understand individual, social and ecological sustainability.Creation-Date: 2010-11

Risk homeostasis theory - A study of intrinsic compensation

Risk homeostasis theory (RHT) suggests that changes made to the intrinsic risk of environments are negated in one of three ways: behavioural adjustments within the environment, mode migration, and avoidance of the physical risk. To date, this three-way model of RHT has little empirical support, whilst research findings on RHT have at times been diametrically opposed. A reconciliation of apparently opposing findings might be possible by suggesting that extrinsic compensation fails to restore previously existing levels of actual risk in cases where behavioural adjustments within the environment are incapable of negating intrinsic risk changes. This paper reports a study in which behavioural adjustments within the physical risk-taking environment are capable of reconciling target with actual risk. The results provide positive support for RHT in the form of overcompensation for the intrinsic risk change on specific driver behaviours

Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator