Decision Markets for Policy Advice

The main cause of bad policy decisions is arguably a lack of information. Decisionmakers often do not make use of relevant information about the consequences of the policies they choose. The problem, however, is not simply that public officials do not exploit readily available information. It is also that they do not take full advantage of creative mechanisms that could expand the supply of policy-relevant information. Among the most innovative and potentially useful information-generating mechanisms are speculative markets. Speculative markets produce public information about the perceived likelihood of future events as a natural byproduct of voluntary exchange. Speculative markets do a remarkable job of aggregating information; in every head-to-head field comparison made so far, their forecasts have been at least as accurate as those of competing institutions, such as official government estimates. Many organizations are now trying to take advantage of this effect, experimenting with the creation of "prediction markets" or "information markets," to forecast future events such as product sales and project completion dates. This chapter examines the uses and limitations of decision markets. Decision markets are information markets designed to inform a particular policy decision, by directly estimating relevant consequences of that decision. After reviewing the weaknesses of existing institutions, the mechanics of decision markets, and a concrete example, this chapter reviews the requirements, advantages, and disadvantages of decision markets. The chapter also takes a close look at a particular application of this tool: the controversial yet illuminating attempt to establish a "Policy Analysis Market" to forecast the consequences of major policy U.S. choices in the Middle East.

Soil Nitrogen and Carbon in Urban and Rural Forests

Previous work by Dr. Nancy Broshot has revealed high tree mortality and low recruitment (new seedlings) in an urban forest (Forest Park in Portland, Oregon). A series of lichen surveys in 2013 showed the lichen community has shifted to one dominated by lichens tolerant of and thriving on high nitrogen levels. To ascertain if nitrogenous air pollution could be a cause of low recruitment, we measured the level of nitrogen and carbon in the soil at 32 sites in Forest Park (24 permanent sites and 8 conifer recruitment sites). We also added 3 control sites in the Mount Hood National Forest above Estacada along an apparent air pollution gradient. The plant community was measured at three transects at each control site and lichen surveys were conducted. Four soil samples were collected at each site, dried at 35oC until weight remained constant and sieved to reduce to fine soil particle size. The samples will be assessed using an elemental analyzer to determine total nitrogen and total carbon

Tree Composition and Seedling Recruitment in Urban and Rural Forests

In 1993, Dr. Nancy Broshot randomly located 25 permanent study sites in Forest Park in Portland, Oregon to examine the effects of urbanization on forest health. Plant community structure was examined. In 2003, Dr. Broshot reexamined the plant communities at each site and found significantly higher tree mortality and reduced recruitment (young trees) in all areas of the park. Many seedlings that had been present in 1993 were absent in 2003. In 2013, a 20-year follow up study of the tree community was conducted. Although the rate of tree mortality had dropped, recruitment of seedlings and saplings was still low. A series of lichen studies completed at each site in 2013 indicated high levels of nitrogenous air pollution at all sites in the park. In 2014, three control sites along a gradient of air quality in the Mount Hood National Forest above Estacada, Oregon were added to the study. Plant community variables were measured in the same manner as in Forest Park. We found significantly more live trees, saplings and seedlings at the control sites than at sites in Forest Park. We also found significantly fewer dead trees at control sites. Indeed, we had more seedlings at the three control sites than at all 25 of the Forest Park sites. We believe the low level of recruitment may be due to nitrogenous deposition from air pollution in Forest Park; we are waiting for results from collected soil samples to evaluate this hypothesis

Bayesian classification theory

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit or share model parameters though a class hierarchy. We summarize the mathematical foundations of AutoClass

ON MARKET MAKER FUNCTIONS

Since market scoring rules have become popular as a form of market maker, it seems worth reviewing just what such mechanisms are intended to do.The main function performed by most market makers is to serve as an intermediary between people who prefer to trade at different times.  Traders who have the same favorite times to trade can show up together to an ordinary continuous double auction, and then make and accept offers to trade.  But when traders have different favorite times, a market maker can help them by first making offers that some of them will accept, and then later making opposite offers which others will accept.  By adjusting prices in his favor, a market maker can even profit from providing this service

Autoclass: An automatic classification system

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized

IF THE LORD\u27S WILLING AND THE CREEK DON\u27T RISE FLOOD CONTROL AND THE DISPLACED RURAL COMMUNITIES OF IRVING AND BROUGHTON, KANSAS

In this case study, I examine how the residents of two displaced rural Kansas towns, and their descendants, exhibit a sense of identity common to small farm communities throughout the Great Plains, and how tenacious these ties are even after the physical reminder of their communal bonds no longer exists. By examining the struggles to survive faced by these two towns, Irving and Broughton, the resiliency of the people who called them home, and the continuing expression of community solidarity by the individuals associated with them, I propose that the individuals living within these communities created a transcendental identity similar to that of Benedict Anderson\u27s Imagined Communities. This communal identity remains a unifying bond without the need for an enduring physical signifier

Can Manipulators Mislead Prediction Market Observers?

We study experimental markets where privately informed traders exchange simple assets, and where uninformed third parties are asked to forecast the values of these assets, guided only by market prices. Although prices only partially aggregate information, they significantly improve the forecasts of third parties. In a second treatment, a portion of traders are given preferences over the forecasts made by observers. Although we find evidence that these traders attempt to manipulate prices in order to influence the beliefs of observers, we find no evidence that observers make less accurate forecasts as a result.