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Experiment 6: the dependence of eudaimonic well-being on the rise and fall of hedonic states in the environments that contain both food and poison. The four plots correspond to the four map types shown in Fig 12 Agents with a more negative outlook (pλ = 2, pλ = 0.5) tend to do better in all four types of environments.
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We offer and test a simple operationalization of hedonic and eudaimonic well-being (“happiness”) as mediating variables that link outcomes to motivation. In six evolutionary agent-based simulation experiments, we compared the relative performance of agents endowed with different combinations of happiness-related traits (parameter values), under fou...
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... Furthermore, evolutionary simulations suggest that performance-driven positive affect alone is not as effective in motivating an agent as an alternation of positive and negative affective states, brought about, respectively, by successes and failures (Gao and Edelman, 2016a); moreover, such a balance between happiness and unhappiness can serve as an effective intrinsic motivator (Gao and Edelman, 2016b). Likewise, in the evolutionary account of pain proposed by Kolodny et al. (2021), the pain factor makes a contribution to reinforcement learning that is orthogonal to that of reward. ...
In its pragmatic turn, the new discipline of AI ethics came to be dominated by humanity's collective fear of its creatures, as reflected in an extensive and perennially popular literary tradition. Dr. Frankenstein's monster in the novel by Mary Shelley rising against its creator; the unorthodox golem in H. Leivick's 1920 play going on a rampage; the rebellious robots of Karel \v{C}apek -- these and hundreds of other examples of the genre are the background against which the preoccupation of AI ethics with preventing robots from behaving badly towards people is best understood. In each of these three fictional cases (as well as in many others), the miserable artificial creature -- mercilessly exploited, or cornered by a murderous mob, and driven to violence in self-defense -- has its author's sympathy. In real life, with very few exceptions, things are different: theorists working on the ethics of AI completely ignore the possibility of robots needing protection from their creators. The present book chapter takes up this, less commonly considered, ethical angle of AI.
... In an evolutionary setting, this assumption makes intuitive sense insofar as (i) reinforcement learning is universally employed by living systems in honing adaptive behavior, and (ii) an autonomous system by definition must provide its own source of drive, as per the principle of intrinsic motivation (Barto, 2013). Furthermore, evolutionary simulations suggest that performance-driven positive affect alone is not as effective in motivating an agent as an alternation of positive and negative affective states, brought about, respectively, by successes and failures (Gao and Edelman, 2016a); moreover, such a balance between happiness and unhappiness can serve as an effective intrinsic motivator (Gao and Edelman, 2016b). If it were possible for the agent to choose not to experience negative affect, suffering would be avoided, but the question still remains whether or not the price for that would be failing to learn quickly and well from the consequences of behavior. ...
Insofar as consciousness has a functional role in facilitating learning and behavioral control, the builders of autonomous AI systems are likely to attempt to incorporate it into their designs. The extensive literature on the ethics of AI is concerned with ensuring that AI systems, and especially autonomous conscious ones, behave ethically. In contrast, our focus here is on the rarely discussed complementary aspect of engineering conscious AI: how to avoid condemning such systems, for whose creation we would be solely responsible, to unavoidable suffering brought about by phenomenal self-consciousness. We outline two complementary approaches to this problem, one motivated by a philosophical analysis of the phenomenal self, and the other by certain computational concepts in reinforcement learning.
In promoting career sustainability, psychological theories historically have informed human resource management (HRM) development—three assessment directions are among them: work-related flow, happiness promotion, and appraising PERMA (Positive Emotions, Engagement, Relationships, Meaning, and Accomplishment) factors. Csikszentmihalyi’s work-related flow represents an optimally challenging work-related process. Happiness promotion strives to maintain a pleased satisfaction with the current experience. PERMA represents measurable positive psychological factors constituting well-being. Reliable and validated, the experience of flow has been found to determine career sustainability in contrast to the more often investigated happiness ascertainment or identifying PERMA factors. Career sustainability research to inform HRM development is in its infancy. Therefore, publishers’ commitment to sustainability provides integrity. Given MDPI’s uniquely founding sustainability concern, its journal articles were searched with the keywords “flow, Csikszentmihalyi, work”, excluding those pertaining to education, health, leisure, marketing, non-workers, and spirituality, to determine the utilization of work-related flow to achieve career sustainability. Of the 628 returns, 28 reports were included for potential assessment. Current studies on Csikszentmihalyi’s work-related flow ultimately represented three results. These provide insight into successful, positive methods to develop career sustainability. Consequently, HRM is advised to investigate practices for assessing and encouraging employees’ engagement with work-related flow with the aim of ensuring career sustainability.
Till date, as consciousness has a functional role in facilitating learning and behavioral control, the builders of autonomous Artificial Intelligence (AI) systems are likely to attempt to incorporate it into their designs. The extensive literature on the ethics of AI is concerned with ensuring that AI systems, and especially autonomous conscious ones, behave ethically. In contrast, our focus here is on the rarely discussed complementary aspect of engineering conscious AI: how to avoid condemning such systems, for whose creation we would be solely responsible, to unavoidable suffering brought about by phenomenal self-consciousness. We outline two complementary approaches to this problem, one motivated by a philosophical analysis of the phenomenal self, and the other by certain computational concepts in reinforcement learning.
Cambridge Core - Statistics for Social Sciences, Behavioral Sciences and Law - Replacing GDP by 2030 - by Rutger Hoekstra
Reinforcement learning, a general and universally useful framework for learning from experience, has been broadly recognized as a critically important concept for understanding and shaping adaptive behavior, both in ethology and in artificial intelligence. A key component in reinforcement learning is the reward function, which, according to an emerging consensus, should be intrinsic to the learning agent and a matter of appraisal rather than a simple reflection of external outcomes. We describe an approach to intrinsically motivated reinforcement learning that involves various aspects of happiness, operationalized as dynamic estimates of well-being. In four experiments, in which simulated agents learned to explore and forage in simulated environments, we show that agents whose reward function properly balances momentary (hedonic) and longer-term (eudaimonic) well-being outperform agents equipped with standard fitness-oriented reward functions. Our findings suggest that happiness-based features can be useful in developing robust, general-purpose reward mechanisms for intrinsically motivated autonomous agents.
