Behavioral Biases in Annuity Choice: An Experiment

We conduct a neutral-context laboratory experiment to systematically investigate the role of the hit-by-bus concern in explaining the annuitization puzzle: the low rate of retirement-asset annuitization relative to the predictions of standard models. We vary endowed asset (annuity vs. stock of wealth vs. no explicit endowment), and find a strong endowment effect. Furthermore, we find that the ordering of survival risks matters. Compared to a frame in which a single draw from a known distribution determines survival outcome, annuity choice is lower when subjects must sequentially survive early periods to reach periods in which the annuity dominates. We conclude with policy implications.experimental economics, behavioral, retirement, annuities

When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting

This study clarifies the conditions under which learning in games produces convergence to Nash equilibria in practice. Previous work has identified theoretical conditions under which various stylized learning processes achieve convergence. One technical condition is supermodularity, which is closely related to the more familiar concept of strategic complementarities. We experimentally investigate the role of supermodularity in achieving convergence through learning. Using a game from the literature on solutions to externalities, we systematically vary a free parameter below, close to, at and beyond the threshold of supermodularity to assess its effects on convergence. We find that supermodular and ¡°near-supermodular¡± games converge significantly better than those far below the threshold. From a little below the threshold to the threshold, the improvement is statistically insignificant. Within the class of supermodular games, increasing the parameter far beyond the threshold does not significantly improve convergence. Simulation shows that while most experimental results persist in the long run, some become more pronounced.learning, supermodular games

Giving Gossips Their Due: Information Provision in Games with Private Monitoring

The ability of a long-lived seller to maintain and profit from a good reputation may induce her to provide high quality or effort despite short-run incentives to the contrary. This incentive remains in place with private monitoring, provided that buyers share their information. However, this assumption is unrealistic in environments where information sharing is costly or the beneficiaries of a buyer’s sharing are strangers. I study a simple mechanism that induces costly information provision, and may explain such behavior in environments where the incentives are not overt. Agents who possess information may share it with the community and acquire a reputation for gossiping. Reputations function in tandem: sellers provide high effort because they face agents with reputations for information sharing, and expect the outcome of their dealings will be made public, while information holders share their information as a reputation for doing so results in higher effort from sellers.reputation, moral hazard, information sharing, mechanism design

System Design, User Cost and Electronic Usage of Journals

Dramatic increases in the capabilities and decreases in the costs of computers and communication networks have fomented revolutionary thoughts in the scholarly publishing community. In one dimension, traditional pricing schemes and product packages are being modified or replaced. We designed and undertook a large-scale field experiment in pricing and bundling for electronic access to scholarly journals: PEAK. We provided Internet-based delivery of content from 1200 Elsevier Science journals to users at multiple campuses and commercial facilities. Our primary research objective was to generate rich empirical evidence on user behavior when faced with various bundling schemes and price structures. In this article we explain the different types and levels of cost that users faced when accessing individual articles, and report on the effect of these costs on usage. We found that both monetary and non-monetary user costs have a significant impact on the demand for electronic access. We also estimate how taking user costs into account would change the optimal (least cost) bundle of access options that an institution should purchase.

Ambiguous Solicitation: Ambiguous Prescription

We conduct a two-phase laboratory experiment, separated by several weeks. In the first phase, we conduct urn games intended to measure ambiguity aversion on a representative population of undergraduate students. In the second phase, we invite the students back with four different solicitation treatments, varying in the ambiguity of information regarding the task and the payout of the laboratory experiment. We find that those who return do not differ from the overall pool with respect to their ambiguity version. However, no solicitation treatment generates a representative sample. The ambiguous task treatment drives away the ambiguity averse disproportionally, and the detailed task treatment draws in the ambiguity averse disproportionally.laboratory experimental methods, experimental economics, laboratory selection effects

Pricing and Bundling Electronic Information Goods: Field Evidence

Dramatic increases in the capabilities and decreases in the costs of computers and communication networks have fomented revolutionary thoughts in the scholarly publishing community. In one dimension, traditional pricing schemes and product packages are being modified or replaced. We designed and undertook a large-scale field experiment in pricing and bundling for electronic access to scholarly journals: PEAK. We provided Internet-based delivery of content from 1200 Elsevier Science journals to users at multiple campuses and commercial facilities. Our primary research objective was to generate rich empirical evidence on user behavior when faced with various bundling schemes and price structures. In this article we report initial results. We found that although there is a steep initial learning curve, decision-makers rapidly comprehended our innovative pricing schemes. We also found that our novel and flexible "generalized subscription" was successful at balancing paid usage with easy access to a larger body of content than was previously available to participating institutions. Finally, we found that both monetary and non-monetary user costs have a significant impact on the demand for electronic access.

Model Selection in an Information Economy : Choosing what to Learn

As online markets for the exchange of goods and services become more common, the study of markets composed at least in part of autonomous agents has taken on increasing importance. In contrast to traditional completeinformation economic scenarios, agents that are operating in an electronic marketplace often do so under considerable uncertainty. In order to reduce their uncertainty, these agents must learn about the world around them. When an agent producer is engaged in a learning task in which data collection is costly, such as learning the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the agent has a limited number of chances to experiment, it must explicitly consider the cost of learning (in terms of foregone profit) against the value of the information acquired. Information goods add an additional dimension to this problem; due to their flexibility, they can be bundled and priced according to a number of different price schedules. An optimizing producer should consider the profit each price schedule can extract, as well as the difficulty of learning of this schedule. In this paper, we demonstrate the tradeoff between complexity and profitability for a number of common price schedules. We begin with a one-shot decision as to which schedule to learn. Schedules with moderate complexity are preferred in the short and medium term, as they are learned quickly, yet extract a significant fraction of the available profit. We then turn to the repeated version of this one-shot decision and show that moderate complexity schedules, in particular two-part tariff, perform well when the producer must adapt to nonstationarity in the consumer population. When a producer can dynamically change schedules as it learns, it can use an explicit decision-theoretic formulation to greedily select the schedule which appears to yield the greatest profit in the next period. By explicitly considering the both the learnability and the profit extracted by different price schedules, a producer can extract more profit as it learns than if it naively chose models that are accurate once learned.Online learning; information economics; model selection; direct search

Improving Learning Performance by Applying Economic Knowledge

Digital information economies require information goods producers to learn how to position themselves within a potentially vast product space. Further, the topography of this space is often nonstationary, due to the interactive dynamics of multiple producers changing their position as they try to learn the distribution of consumer preferences and other features of the problem's economic structure. This presents a producer or its agent with a difficult learning problem: how to locate profitable niches in a very large space. In this paper, we present a model of an information goods duopoly and show that, under complete information, producers would prefer not to compete, instead acting as local monopolists and targeting separate niches in the consumer population. However, when producers have no information about the problem they are solving, it can be quite difficult for them to converge on this solution. We show how a modest amount of economic knowledge about the problem can make it much easier, either by reducing the search space, starting in a useful area of the space, or introducing a gradient. These experiments support the hypothesis that a producer using some knowledge of a problem's (economic) structure can outperform a producer that is performing a naive, knowledge-free form of learning.

User cost, usage and library purchasing of electronically-accessed journals

http://deepblue.lib.umich.edu/bitstream/2027.42/63447/3/gazzale-jmm-user-cost-peak-book.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/63447/2/gazzale-jmm-peak-usercost_files.ziphttp://deepblue.lib.umich.edu/bitstream/2027.42/63447/1/gazzale-jmm-peak-usercost.htm

Improving Learning Performance by Applying Economic Knowledge

Digital information economies require information goods producers to learn how to position themselves within a potentially vast product space. Further, the topography of this space is often nonstationary, due to the interactive dynamics of multiple producers changing their positions as they try to learn the distribution of consumer preferences and other features of the problem's economic structure. This presents a producer or its agent with a difficult learning problem: how to locate profitable niches in a very large space. In this paper, we present a model of an information goods duopoly and show that, under complete information, producers would prefer not to compete, instead acting as local monopolists and targeting separate niches in the consumer population. However, when producers have no information about the problem they are solving, it can be quite difficult for them to converge on this solution. We show how a modest amount of economic knowledge about the problem can make it much easier, either by reducing the search space, starting in a useful area of the space, or by introducing a gradient. These experiments support the hypothesis that a producer using some knowledge of a problem's (economic) structure can outperform a producer that is performing a naive, knowledge-free form of learning.http://deepblue.lib.umich.edu/bitstream/2027.42/50435/1/improving-amec03-lncs04.pd