60 research outputs found
The generalized stochastic preference choice model
We propose a new discrete choice model that generalizes the random utility
model (RUM). We show that this model, called the \emph{Generalized Stochastic
Preference} (GSP) model can explain several choice phenomena that can't be
represented by a RUM. In particular, the model can easily (and also exactly)
replicate some well known examples that are not RUM, as well as controlled
choice experiments carried out since 1980's that possess strong regularity
violations. One such regularity violation is the \emph{decoy effect} in which
the probability of choosing a product increases when a similar, but inferior
product is added to the choice set. An appealing feature of the GSP is that it
is non-parametric and therefore it has very high flexibility. The model has
also a simple description and interpretation: it builds upon the well known
representation of RUM as a stochastic preference, by allowing some additional
consumer types to be non-rational
Bargaining Mechanisms for One-Way Games
We introduce one-way games, a framework motivated by applications in
large-scale power restoration, humanitarian logistics, and integrated
supply-chains. The distinguishable feature of the games is that the payoff of
some player is determined only by her own strategy and does not depend on
actions taken by other players. We show that the equilibrium outcome in one-way
games without payments and the social cost of any ex-post efficient mechanism,
can be far from the optimum. We also show that it is impossible to design a
Bayes-Nash incentive-compatible mechanism for one-way games that is
budget-balanced, individually rational, and efficient. To address this negative
result, we propose a privacy-preserving mechanism that is incentive-compatible
and budget-balanced, satisfies ex-post individual rationality conditions, and
produces an outcome which is more efficient than the equilibrium without
payments. The mechanism is based on a single-offer bargaining and we show that
a randomized multi-offer extension brings no additional benefit.Comment: An earlier, shorter version of this paper appeared in Proceedings of
the Twenty-Fourth International joint conference on Artificial Intelligence
(IJCAI) 201
Optimizing Expected Utility in a Multinomial Logit Model with Position Bias and Social Influence
Motivated by applications in retail, online advertising, and cultural
markets, this paper studies how to find the optimal assortment and positioning
of products subject to a capacity constraint. We prove that the optimal
assortment and positioning can be found in polynomial time for a multinomial
logit model capturing utilities, position bias, and social influence. Moreover,
in a dynamic market, we show that the policy that applies the optimal
assortment and positioning and leverages social influence outperforms in
expectation any policy not using social influence
Measuring and Optimizing Cultural Markets
Social influence has been shown to create significant unpredictability in
cultural markets, providing one potential explanation why experts routinely
fail at predicting commercial success of cultural products. To counteract the
difficulty of making accurate predictions, "measure and react" strategies have
been advocated but finding a concrete strategy that scales for very large
markets has remained elusive so far. Here we propose a "measure and optimize"
strategy based on an optimization policy that uses product quality, appeal, and
social influence to maximize expected profits in the market at each decision
point. Our computational experiments show that our policy leverages social
influence to produce significant performance benefits for the market, while our
theoretical analysis proves that our policy outperforms in expectation any
policy not displaying social information. Our results contrast with earlier
work which focused on showing the unpredictability and inequalities created by
social influence. Not only do we show for the first time that dynamically
showing consumers positive social information under our policy increases the
expected performance of the seller in cultural markets. We also show that, in
reasonable settings, our policy does not introduce significant unpredictability
and identifies "blockbusters". Overall, these results shed new light on the
nature of social influence and how it can be leveraged for the benefits of the
market
Popularity signals in trial-offer markets with social influence and position bias
This paper considers trial-offer markets where consumer preferences are modelled by a multinomial logit with social influence and position bias. The social signal for a product is given by its current market share raised to power r (or, equivalently, the number of purchases raised to the power of r). The paper shows that, when r is strictly between 0 and 1, and a static position assignment (e.g., a quality ranking) is used, the market converges to a unique equilibrium where the market shares depend only on product quality, not their initial appeals or the early dynamics. When r is greater than 1, the market becomes unpredictable. In many cases, the market goes to a monopoly for some product: which product becomes a monopoly depends on the initial conditions of the market. These theoretical results are complemented by an agent-based simulation which indicates that convergence is fast when r is between 0 and 1, and that the quality ranking dominates the well-known popularity ranking in terms of market efficiency. These results shed a new light on the role of social influence which is often blamed for unpredictability, inequalities, and inefficiencies in markets. In contrast, this paper shows that, with a proper social signal and position assignment for the products, the market becomes predictable, and inequalities and inefficiencies can be controlled appropriately
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