In multiple pre-registered experiments, we examine the effect of sequences positive and negative experiences on altruism, trust, trustworthiness, cooperation. For non-social experiences, find no subsequent behavior in any these social domains. However, when are nature, more cooperation after gains than losses. neutral with equalizing losses, evidence for a differential irrespective whether expe...

Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...

A longstanding goal of artificial intelligence is to create agents capable learning perform tasks that require sequential decision making. Importantly, while it the agent learns and acts, still up humans specify particular task be performed. Classical task-specification approaches typically involve providing stationary reward functions or explicit demonstrations desired tasks. However, there ha...

This paper is devoted to sequential decision making with Rank Dependent expected Utility (RDU). criterion generalizes Expected and enables model a wider range of observed (rational) behaviors. In such setting, two conflicting objectives can be identified in the assessment strategy: maximizing performance viewed from initial state (optimality), minimizing incentive deviate during implementation ...