Individual attitudes to risk are examined in an evolutionary model. Males obtain more offspring as a consequence of greater wealth both directly and because this attracts more mates. The second effect induces gambling driven by relative wealth and can create Pareto-inefficiency. Fair bets involving small losses and large gains are taken if any fair bets at all are taken. Altogether, the model accords well with observations concerning bets taken and declined by typical individuals despite settings of widely varying per capita wealth.
A biological model is developed here to determine the fittest attitude to risk. With a fixed environment, the type maximizing expected offspring is selected. This yields the expected utility theorem when translated into a criterion for evaluating gambles over commodities. With a random environment, however, the type selected is strictly less averse to idiosyncratic risk than to risk which is correlated across all individuals. The implied criterion for choice over gambles does not satisfy the expected utility theorem and may induce choice of a gamble which is first-order stochastically dominated.
This paper investigates the sensitivity of recent evolutionary models of learning to the specification of the matching mechanism. We study a literally random matching mechanism, combined with a process of strategy adjustment based on the realized average performance of each strategy. In the key class of symmetric 2x2 coordination games, the Pareto-efficient equilibrium, per se, is selected, rather than the risk-dominant equilibrium, as the probability of experimentation (or ``mutation'') goes to zero. Furthermore, convergence to the equilibrium is relatively fast. We extend these results, for example, to games of common interest.
This paper presents an unconventional argument based on population growth to bolster marginal productivity theory. There is an economy with a single output produced from a number of different types of labor. Each type of labor is reproduced from that type itself and from the amount of the output devoted to it under some income distributional norm. Any norm which fails to induce convergence to maximal balanced growth is ''growth dominated'' in that the population and income it induces can be overwhelmed eventually. On the maximal balanced growth path, the norm divides output according to marginal productivity.
This paper considers a class of naive adaptive learning rules in a social setting. They generalize biological selection and have become relevant in economic theory as a consequence of their use in evolutionary game models. The environment considered here is non-strategic but includes gambles which are more or less completely observed in each period. In the long run, individuals are more averse to a gamble which is less observable, other things equal, and may violate first-order stochastic dominance. Thus these rules need not be consistent with rational behavior in the usual sense.
Gul and Pearce argue that forward induction has much less power as an equilibrium refinement than is generally supposed. The present comment raises the issue: What does admissibility imply for Gul and Pearce's analysis? In a key example, the precise equilibrium constructed by Gul and Pearce relies on a strategy which is not admissible. Even if all equilibria are considered, it is not possible to preserve the Gul and Pearce results under admissibility.
Patterns of reproductive uncertainty can have an important influence on population dynamics. There is a crucial distinction between what we describe here as aggregate uncertainty (in which reproductive output in each generation is correlated among the individuals in a population) and idiosyncratic risk (in which reproductive output is independent across individuals). All else being equal, populations experiencing idiosyncratic risk have a competitive advantage over those experiencing aggregate uncertainty in that they enjoy a higher asymptotic growth rate. Applying this distinction to models of randomly fluctuating environments, we point out that genetic variation among offspring can serve to reduce aggregate uncertainty, transforming it into a more idiosyncratic form of risk. We show that this transformation underlies the dynamics observed in several previous models of the role of outcrossing in the evolution of sex.
This paper considers the biological derivation of von Neumann Morgenstern utility functions. On the one hand, if individuals possess an explicit utility function stemming from the rate of production of expected offspring, they can readily adapt to novelty in a two-armed bandit problem. Embedding such a function in a simple rule of thumb involving no beliefs about prior or posterior probabilities leads to maximization of expected offspring, in a certain limit as the number of repetitions tends to infinity. In general, on the other hand, if any rule yields such evolutionary optimality of behavior, this biological utility function is implicit at least.
This paper first considers the implications of
biological
evolution for economic preferences. It analyzes why utility
functions
evolved, considers evidence that utility is both hedonic and adaptive
and
suggests why such adaptation might have evolved. Time preference
and attitudes to risk are treated—in particular, whether the former is
exponential and the latter are selfish. Arguments for another
form
of interdependence—a concern with status—are treated. The paper
then
considers the evolution of rationality. One hypothesis examined
is
that human intelligence and longevity were forged by hunter-gatherer
economies;
another is that intelligence was spurred by competitive social
interactions.
This paper considers how biological evolution shaped
the
elements of a simple but complete model of economic
decision-making.
These elements are preferences, beliefs and rationality. Whereas
Nature should impose preferences over consumption on the individual,
Nature
should allow beliefs to be influenced by local knowledge, and final
choice
to be flexible. This reinforces the usual approach.
However,
on the one hand, evolution also suggests that some extensions of this
model
are implausible; on the other, it suggests unexpected directions of
generalization.
In any case, evolution provides a basis for an overarching economic
theory
and maintains restrictions on this theory.
Two striking differences between humans and our
closest
living relatives, chimpanzees and gorillas, are the size of our brains
(larger by a factor of three or four) and our life span (longer by a
factor
of about two). Our thesis is that these two distinctive features of
humans
are products of coevolutionary selection. The large human brain is an
investment
with initial costs and later rewards, which coevolved with increased
energy
allocations to survival. Not only does this theory help explain life
history
variation among primates and its extreme evolution in humans; it also
provides
new insight into the evolution of longevity in other biological
systems.
We introduce and apply a general formal demographic model for
constrained
growth and evolutionary tradeoffs in the presence of life-cycle
transfers
between age groups in a population.
A prior signalling stage is added to the prisoner's
dilemma
and the overall population involved is divided into a number of
subpopulations.
Evolution involves both local and global imitation---so that the
process
is formally one of ''group selection.'' A subpopulation that is not
signalling
and defecting against one and all can be invaded by two ''secret
handshake''
mutants. A subpopulation that is composed entirely of the secret
handshake
strategy can be invaded by a single ``sucker punch'' mutant.
Nevertheless,
if there are at least three subpopulations, the population cooperates
always,
in the limit as the mutation rate tends to zero.
This paper presents an evolutionary economic
framework
for a general theory of lifespan, with a particular focus on humans.
The
principal argument is that lifespans evolve as part of a larger,
integrated
life history program and that the program for development and
reproduction
is fundamentally related to the age at which death becomes imminent due
to physiological deterioration. A series of empirical findings suggests
that humans have a species-specific life course with characteristic
schedules
of growth, development, fertility, mortality and aging, based on a set
of specialized anatomical, physiological and psychological adaptations
to the niche humans occupied during their evolutionary history.
Together,
those adaptations result in a species-typical lifespan that can vary
within
a limited range. Our theory is that large brains and slow life
histories
result from a dietary specialization that has characterized the last
two
million years of human evolutionary history. The paper concludes with a
discussion of two themes: short and long term flexibility in the human
lifespan and the building blocks for a more adequate theory of
senescence
and lifespan.
The economics of hunting and gathering must have
driven
the biological evolution of human characteristics, since
hunter-gatherer
societies prevailed for the two million years of human history. These
societies
feature huge intergenerational resource flows, suggesting that these
resource
flows should replace fertility as the key demographic considerations.
It
is then theoretically expected that life expectancy and brain size
would
increase simultaneously, as apparently occurred during our evolutionary
history. The brain here is considered as a direct form of bodily
investment,
but also crucially as facilitating further indirect investment by means
of learning-by-doing.
Strategic rationality is subjected here to natural
selection.
In a zero-sum repeated game of incomplete information, one long-run
individual
is informed of the state of the world, and plays against a sequence of
short-run opponents who are not. Strategies are noisy and have bounded
recall. An equilibrium in these is shown to exist. Relative to any such
equilibrium, sufficiently greater recall enjoys an advantage that is
not
decreasing in the original level of recall, thus capturing the Red
Queen
effect. The selection pressure to reduce a small amount of noise is
less
than that to increase recall.
A short proof of more general version of Harsanyi's
purification theorem is provided through an application of a powerful,
yet intuitive, result from algebraic topology.