Abstract We provide two characterizations, one axiomatic and the other neuro-computational, of the dependence of choice probabilities on deadlines, within the widely used softmax representation $$\beginalign* p_t\left( a,A\right) =\dfrace^\fracu\left( a\right) λ\left( t\right) +α\left( a\right) \sum_b\in Ae^\fracu\left( b\right) λ\left( t\right) +α\left( b\right) , \endalign*$$ where $p_t\left( a,A\right)$ is the probability that alternative a is selected from the set A of feasible alternatives if t is the time available to decide, λ is a time-dependent noise parameter measuring the unit cost of information, u is a time-independent utility function, and α is an alternative-specific bias that determines the initial choice probabilities (reflecting prior information and memory anchoring). Our axiomatic analysis provides a behavioural foundation of softmax (also known as Multinomial Logit Model when α is constant). Our neuro-computational derivation provides a biologically inspired algorithm that may explain the emergence of softmax in choice behaviour. Jointly, the two approaches provide a thorough understanding of softmaximization in terms of internal causes (neuro-physiological mechanisms) and external effects (testable implications).

We extend a standard model of parental investment and intergenerational mobility to include a fully specified genetic analysis of skill transmission. The model’s predictions differ substantially from the standard model’s. The coefficient of intergenerational income elasticity (IGE) may be larger than that in the standard model and depends on the distribution of the genotype. The distribution of genetic endowments may be stratified according to income. The model is tested on data, including genetic information, of twins and their parents, estimating how IGE is affected by genetic factors and how environment and genes interact. The effect of intelligence is substantially stronger than that of other traits.