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Found 6 papers, 0 papers with code
Learning to Maximize (Expected) Utility
We study if participants in a choice experiment learn to behave in ways that are closer to the predictions of ordinal and expected utility theory as they make decisions from the same menus repeatedly and without receiving feedback of any kind.
Non-diversified portfolios with subjective expected utility
Although diversification is the typical strategy followed by risk-averse investors, non-diversified positions that allocate all resources to a single asset, state of the world or revenue stream are common too.
Towards Eliciting Weak or Incomplete Preferences in the Lab: A Model-Rich Approach
Second, it analyses the collected data with a new non-parametric combinatorial-optimization method that allows to compare the goodness-of-fit of utility maximization to that of models of undominated or dominant choice with incomplete preferences, in each case with or without indifferences.
Ordinal Intensity-Efficient Allocations
We study the assignment problem in situations where, in addition to having ordinal preferences, agents also have *ordinal intensities*: they can make simple and internally consistent comparisons such as "I prefer $a$ to $b$ more than I prefer $c$ to $d$" without necessarily being able to quantify them.
The Decision-Conflict Logit
We introduce the *decision-conflict logit*, a simple and disciplined extension of the logit with an outside option that assigns a menu-dependent utility to that option.
Status Quo Bias and the Decoy Effect: A Comparative Analysis in Choice under Risk
We find strong evidence for status quo bias and no evidence for the decoy effect.
