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Psychological and Situational Drivers of Over-Retention in Large-Scale Household Sorting

  Psychological and Situational Drivers of Over-Retention in Large-Scale Household Sorting An analytical report on biases, decision delay, and a low-risk keep-versus-discard matrix Research report 20 March 2026 Prepared in formal academic English for a broad interdisciplinary audience Abstract This report analyses why people sorting large volumes of household possessions often overestimate the benefits of keeping items, underestimate the costs of retention, and postpone disposal decisions. The report addresses the problem as a bounded-rationality challenge that emerges under uncertainty, time pressure, and cumulative decision load. Its aim is not to treat all keeping as error, but to identify the conditions under which systematic bias is likely and to propose a simple decision matrix that reduces such errors without exposing genuinely valuable, irreplaceable, or identity-relevant items to unnecessary disposal. The analysis synthesizes research from behavioura...
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Is a Linear Conception of Time Sufficient in Computational Psychology?

  Is a Linear Conception of Time Sufficient in Computational Psychology? A Critical Analytical Report                 Analytical research report 20 March 2026   Abstract This report examines whether a linear conception of time is sufficient in computational psychology and, if not, in what precise sense it becomes insufficient. The central finding is that the answer depends on which aspect of “linearity” is under discussion. A single linear temporal order - earlier versus later - is almost always indispensable, and a single external time axis is often a useful modeling convention. In several major model families, including diffusion decision models, Kalman-style state-space models, and standard reinforcement learning, linear time indexing is not itself the limiting assumption. The more substantive problems arise when one assumes that a single homogeneous clock is enough for all relevant processes, that m...
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