Search Results

The Introduction presents the main puzzle of the book: why do ordinary people take up trading when statistical evidence shows that a vast majority of them do not make any money? Starting with an argument initially made in financial theory, namely that markets need “noise” in order to work, it asks, How does this “noise” look like in practice? It introduces the distinction between informed or professional traders, on the one hand, and noise or retail traders, on the other hand, and questions it from a sociological point of view. The Introduction shows how the meaning of the financial concept of “noise” has changed over decades, to designate a particular category of traders as distinct from professional ones. This raises the issue of how various categories of traders are constituted, how they relate to each other, and how such links can be investigated within electronic markets. The Introduction lays out the ethnographical approach to investigating categories and types of traders, in relationship to the institutions which support trading. It argues that the ethnography of electronic trading and of retail traders cannot be limited to online exchanges, but needs to take into account the manifold institutions and interactions which constitute markets.

This chapter suggests that prediction markets can provide objective gauges of expert consensus for the media to pass along to the public. Experience suggests that prediction markets are generally fairly accurate, and, at least, they provide incentives for those who could beat the market to push predictions in a sensible direction. The chapter describes a relatively simple structure for a prediction market that can be used to estimate the probability that a particular event will occur. After recounting the experiences of perhaps the most famous prediction market, the nonprofit Iowa Electronic Markets (IEM), the chapter elaborates a more general type of numeric prediction market that the IEM also uses. The chapter also argues that prediction markets offer considerable advantages and also present unique challenges. The chapter suggests how the media might use prediction markets to provide better information to readers and viewers.

Organisations and individuals trading in electronic markets are increasingly using automated agents. Agents are particularly useful in markets where trade might not otherwise have been possible, for example because a lot of information must be processed quickly, or because employing human traders in 24-hour, small transaction markets is not cost-effective. Markets for communication bandwidth, where organisations trade the rights to transmit data over a network, are one such application. Because demand fluctuates considerably every few seconds, agent-based spot markets provide extra liquidity. In order for such markets to work effectively, it is necessary to provide users with agents that can represent and maximise the goals of the organisations using them. This is a hard problem. This chapter provides general principles and examples of good algorithms, based on our research in this area and on our work on designing agents and markets for trading communication bandwidth — some of which on behalf of HP labs. The contribution of our approach is threefold: First, the chapter provides a definition for what a ‘good’ trading agent is. Second, the chapter discusses how to design an interface where users can easily and effectively communicate their preferences to the agents. Clearly, if users can not do that, then this will limit both their incentives to use such technologies and the benefits of what they can achieve when they so. Third, the chapter shows how to design adaptive agents based on ideas from statistical decision theory. In particular our agents are designed to differentiate stable from unstable market conditions and to best respond to these changes. The chapter describes simulation results which show that our design performs well in a large number of trading environments which tries to capture what real markets trading is like.

This chapter evaluates the impact of the availability of electronic labor markets on the university-to-work transition. In particular, the effects of a specific electronic intermediary, the interuniversity consortium AlmaLaurea, on graduates' unemployment, mobility, and matching quality are studied. In a nutshell, AlmaLaurea collects and organizes online information concerning college graduates' curricula and conditional on their permission, sells it to firms in electronic format. The AlmaLaurea recruitment service turns out to be an insightful example concerning how online communication technologies—coupled with more traditional forms of intermediation—might ameliorate the way in which employers and employees match in the labor market. The single characteristic of AlmaLaurea that possibly made it a successful intermediary is also the most unusual—member universities certify the information contained in electronic curricula and also provide some information on the entire population of graduates.

This chapter describes how markets that use a combination of automated and human agents can work. It presents a system AutONA (Automated One-to-one Negotiation Agent), for conducting multiple simultaneous negotiations over price and quantity. The use of competition between sellers to guide negotiation tactics is key. This system was implemented and human trials were conducted to evaluate it based on its ability to negotiate ‘reasonably’, and on its performance with respect to a trading game that was designed independently of the system itself. AutONA passed a limited version of the Turing test: The experiments did not reveal any obvious exploitation that a human trader can use against AutONA.