Harmful Freedom of Choice: Lessons from the Cellphone Market

This article focuses on the relationship between provider and customer, specifically on the complexity of available contracts in the cellphone market and the ways this complexity might be harmful to consumers. This article aims to elucidate the issues, fleshing them out both as a general phenomenon and as a specific implementation in the cellphone context. The aim is not to provide ultimate solutions, but to show the directions these solutions might take and the difficulties involved

Study on Evolvement Complexity in an Artificial Stock Market

An artificial stock market is established based on multi-agent . Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. Simulation results exhibit that large events are frequent in the fluctuation of the stock price generated by the present model when compared with a normal process, and the price returns distribution is L\'{e}vy distribution in the central part followed by an approximately exponential truncation. In addition, by defining a variable to gauge the "evolvement complexity" of this system, we have found a phase cross-over from simple-phase to complex-phase along with the increase of the number of individuals, which may be a ubiquitous phenomenon in multifarious real-life systems.Comment: 4 pages and 4 figure

Platforms, \u3ci\u3eAmerican Express\u3c/i\u3e, and the Problem of Complexity in Antitrust

I. Introduction II. The Great Generalization III. Antitrust Without Platform Theory ... A. Are Credit Cards Really a Boon to Society and Would Non-Platform Antitrust Wreck It? ... 1. Do Credit Cards Do Anything Special? ... 2. How a Platform Player Causes Harm on One Side ... B. Will the Cat Really Stay in the Credit Card Bag? IV. Conclusion: How Antitrust Complexity Devolves to Conservative Simplicit

Inattentive Consumers in Markets for Services

In an experiment on markets for services, we find that consumers are likely to stick to default tariffs and achieve suboptimal outcomes. We find that inattention to the task of choosing a better tariff is likely to be a substantial problem in addition to any task and tariff complexity effect. The institutional setup on which we primarily model our experiment is the UK electricity and gas markets, and our conclusion is that the new measures by the UK regulator Ofgem to improve consumer outcomes are likely to be of limited impact

Approximately Optimal Mechanism Design: Motivation, Examples, and Lessons Learned

Optimal mechanism design enjoys a beautiful and well-developed theory, and also a number of killer applications. Rules of thumb produced by the field influence everything from how governments sell wireless spectrum licenses to how the major search engines auction off online advertising. There are, however, some basic problems for which the traditional optimal mechanism design approach is ill-suited --- either because it makes overly strong assumptions, or because it advocates overly complex designs. The thesis of this paper is that approximately optimal mechanisms allow us to reason about fundamental questions that seem out of reach of the traditional theory. This survey has three main parts. The first part describes the approximately optimal mechanism design paradigm --- how it works, and what we aim to learn by applying it. The second and third parts of the survey cover two case studies, where we instantiate the general design paradigm to investigate two basic questions. In the first example, we consider revenue maximization in a single-item auction with heterogeneous bidders. Our goal is to understand if complexity --- in the sense of detailed distributional knowledge --- is an essential feature of good auctions for this problem, or alternatively if there are simpler auctions that are near-optimal. The second example considers welfare maximization with multiple items. Our goal here is similar in spirit: when is complexity --- in the form of high-dimensional bid spaces --- an essential feature of every auction that guarantees reasonable welfare? Are there interesting cases where low-dimensional bid spaces suffice?Comment: Based on a talk given by the author at the 15th ACM Conference on Economics and Computation (EC), June 201

The influence of project complexity on estimating accuracy

With the rapid development in technology over recent years, construction, in common with many areas of industry, has become increasingly complex. It would, therefore, seem to be important to develop and extend the understanding of complexity so that industry in general and in this case the construction industry can work with greater accuracy and efficiency to provide clients with a better service. This paper aims to generate a definition of complexity and a method for its measurement in order to assess its influence upon the accuracy of the quantity surveying profession in UK new build office construction. Quantitative data came from an analysis of twenty projects of varying size and value and qualitative data came from interviews with professional quantity surveyors. The findings highlight the difficulty in defining and measuring project complexity. The correlation between accuracy and complexity was not straightforward, being subjected to many extraneous variables, particularly the impact of project size. Further research is required to develop a better measure of complexity. This is in order to improve the response of quantity surveyors, so that an appropriate level of effort can be applied to individual projects, permitting greater accuracy and enabling better resource planning within the profession

How to Solve an Allocation Problem?

Game theory proposes several allocation solutions: we know (a) fairness properties, (b) how to develop (c) methods building on these properties, and (d) how to calculate (e) allocations. We also know how to influence the perceived fairness and realization of allocation solutions. However, we cannot explain properly that theoretically fair allocation methods are rarely used.\ud To obtain more insight into these issues we solved an allocation problem in a purchasing cooperative case study by confronting theory with perceptions. We find large theoretical and perception differences and inconsistencies between and within the five steps from a to e. We note that theoretically fair methods tend to be more complex than theoretically unfair methods. In addition, the allocations of some simple methods are perceived fairer than the allocations of complex methods in our case study. To improve theoretical solutions the focus should be on a and c. To influence perceptions the focus should be on b, c, and d. Finally, all five steps are modeled into comparable fairness measures and a general model. Using this model implies that both theory and perceptions are considered in solving allocation problems