73 research outputs found
How Bad is Forming Your Own Opinion?
The question of how people form their opinion has fascinated economists and
sociologists for quite some time. In many of the models, a group of people in a
social network, each holding a numerical opinion, arrive at a shared opinion
through repeated averaging with their neighbors in the network. Motivated by
the observation that consensus is rarely reached in real opinion dynamics, we
study a related sociological model in which individuals' intrinsic beliefs
counterbalance the averaging process and yield a diversity of opinions.
By interpreting the repeated averaging as best-response dynamics in an
underlying game with natural payoffs, and the limit of the process as an
equilibrium, we are able to study the cost of disagreement in these models
relative to a social optimum. We provide a tight bound on the cost at
equilibrium relative to the optimum; our analysis draws a connection between
these agreement models and extremal problems that lead to generalized
eigenvalues. We also consider a natural network design problem in this setting:
which links can we add to the underlying network to reduce the cost of
disagreement at equilibrium
Planning Problems for Sophisticated Agents with Present Bias
Present bias, the tendency to weigh costs and benefits incurred in the
present too heavily, is one of the most widespread human behavioral biases. It
has also been the subject of extensive study in the behavioral economics
literature. While the simplest models assume that the agents are naive,
reasoning about the future without taking their bias into account, there is
considerable evidence that people often behave in ways that are sophisticated
with respect to present bias, making plans based on the belief that they will
be present-biased in the future. For example, committing to a course of action
to reduce future opportunities for procrastination or overconsumption are
instances of sophisticated behavior in everyday life.
Models of sophisticated behavior have lacked an underlying formalism that
allows one to reason over the full space of multi-step tasks that a
sophisticated agent might face. This has made it correspondingly difficult to
make comparative or worst-case statements about the performance of
sophisticated agents in arbitrary scenarios. In this paper, we incorporate the
notion of sophistication into a graph-theoretic model that we used in recent
work for modeling naive agents. This new synthesis of two formalisms -
sophistication and graph-theoretic planning - uncovers a rich structure that
wasn't apparent in the earlier behavioral economics work on this problem.
In particular, our graph-theoretic model makes two kinds of new results
possible. First, we give tight worst-case bounds on the performance of
sophisticated agents in arbitrary multi-step tasks relative to the optimal
plan. Second, the flexibility of our formalism makes it possible to identify
new phenomena that had not been seen in prior literature: these include a
surprising non-monotonic property in the use of rewards to motivate
sophisticated agents and a framework for reasoning about commitment devices
Economic Efficiency Requires Interaction
We study the necessity of interaction between individuals for obtaining
approximately efficient allocations. The role of interaction in markets has
received significant attention in economic thinking, e.g. in Hayek's 1945
classic paper.
We consider this problem in the framework of simultaneous communication
complexity. We analyze the amount of simultaneous communication required for
achieving an approximately efficient allocation. In particular, we consider two
settings: combinatorial auctions with unit demand bidders (bipartite matching)
and combinatorial auctions with subadditive bidders. For both settings we first
show that non-interactive systems have enormous communication costs relative to
interactive ones. On the other hand, we show that limited interaction enables
us to find approximately efficient allocations
Planning with Multiple Biases
Recent work has considered theoretical models for the behavior of agents with
specific behavioral biases: rather than making decisions that optimize a given
payoff function, the agent behaves inefficiently because its decisions suffer
from an underlying bias. These approaches have generally considered an agent
who experiences a single behavioral bias, studying the effect of this bias on
the outcome.
In general, however, decision-making can and will be affected by multiple
biases operating at the same time. How do multiple biases interact to produce
the overall outcome? Here we consider decisions in the presence of a pair of
biases exhibiting an intuitively natural interaction: present bias -- the
tendency to value costs incurred in the present too highly -- and sunk-cost
bias -- the tendency to incorporate costs experienced in the past into one's
plans for the future.
We propose a theoretical model for planning with this pair of biases, and we
show how certain natural behavioral phenomena can arise in our model only when
agents exhibit both biases. As part of our model we differentiate between
agents that are aware of their biases (sophisticated) and agents that are
unaware of them (naive). Interestingly, we show that the interaction between
the two biases is quite complex: in some cases, they mitigate each other's
effects while in other cases they might amplify each other. We obtain a number
of further results as well, including the fact that the planning problem in our
model for an agent experiencing and aware of both biases is computationally
hard in general, though tractable under more relaxed assumptions
Mechanism Design with Moral Bidders
A rapidly growing literature on lying in behavioral economics and psychology
shows that individuals often do not lie even when lying maximizes their
utility. In this work, we attempt to incorporate these findings into the theory
of mechanism design. We consider players that have a preference for
truth-telling and will only lie if their benefit from lying is sufficiently
larger than the loss of the others. To accommodate such players, we introduce
-moral mechanisms, in which the gain of a player from misreporting his
true value, comparing to truth-telling, is at most times the loss that
the others incur due to misreporting.
We develop a theory of moral mechanisms in the canonical setting of
single-item auctions. We identify similarities and disparities to the standard
theory of truthful mechanisms. In particular, we show that the allocation
function does not uniquely determine the payments and is unlikely to admit a
simple characterization. In contrast, recall that monotonicity characterizes
the allocation function of truthful mechanisms.
Our main technical effort is invested in determining whether the auctioneer
can exploit the preference for truth-telling of the players to extract more
revenue comparing to truthful mechanisms. We show that the auctioneer can
extract more revenue when the values of the players are correlated, even when
there are only two players. However, we show that truthful mechanisms are
revenue-maximizing even among moral ones when the values of the players are
independently drawn from certain identical distributions. As a by product we
get an alternative proof to Myerson's characterization in the settings that we
consider. We flesh out this approach by providing an alternative proof to
Myerson's characterization that does not involve moral mechanisms whenever the
values are independently drawn from regular distributions
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