63,251 research outputs found
Budget constraints in prediction markets
We give a detailed characterization of optimal trades under budget constraints in a prediction market with a cost-function-based automated market maker. We study how the budget constraints of individual traders affect their ability to impact the market price. As a concrete application of our characterization, e give sufficient conditions for a property we call budget additivity: two traders with budgets B and B0 and the same beliefs would have a combined impact equal to a single trader with budget B +B0. That way, even if a single trader cannot move the market much, a crowd of like-minded traders can have the same desired effect. When the set of payoff vectors associated with outcomes, with coordinates corresponding to securities, is affinely independent, we obtain that a generalization of the heavily-used logarithmic market scoring rule is budget additive, but the quadratic market scoring rule is not. Our results may be used both descriptively, to understand if a particular market maker is affected by budget constraints or not, and prescriptively, as a recipe to construct markets.postprin
Multi-outcome and Multidimensional Market Scoring Rules
Hanson's market scoring rules allow us to design a prediction market that
still gives useful information even if we have an illiquid market with a
limited number of budget-constrained agents. Each agent can "move" the current
price of a market towards their prediction.
While this movement still occurs in multi-outcome or multidimensional markets
we show that no market-scoring rule, under reasonable conditions, always moves
the price directly towards beliefs of the agents. We present a modified version
of a market scoring rule for budget-limited traders, and show that it does have
the property that, from any starting position, optimal trade by a
budget-limited trader will result in the market being moved towards the
trader's true belief. This mechanism also retains several attractive strategic
properties of the market scoring rule
Information Aggregation in Exponential Family Markets
We consider the design of prediction market mechanisms known as automated
market makers. We show that we can design these mechanisms via the mold of
\emph{exponential family distributions}, a popular and well-studied probability
distribution template used in statistics. We give a full development of this
relationship and explore a range of benefits. We draw connections between the
information aggregation of market prices and the belief aggregation of learning
agents that rely on exponential family distributions. We develop a very natural
analysis of the market behavior as well as the price equilibrium under the
assumption that the traders exhibit risk aversion according to exponential
utility. We also consider similar aspects under alternative models, such as
when traders are budget constrained
Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints
We study a problem of optimal information gathering from multiple data
providers that need to be incentivized to provide accurate information. This
problem arises in many real world applications that rely on crowdsourced data
sets, but where the process of obtaining data is costly. A notable example of
such a scenario is crowd sensing. To this end, we formulate the problem of
optimal information gathering as maximization of a submodular function under a
budget constraint, where the budget represents the total expected payment to
data providers. Contrary to the existing approaches, we base our payments on
incentives for accuracy and truthfulness, in particular, {\em peer-prediction}
methods that score each of the selected data providers against its best peer,
while ensuring that the minimum expected payment is above a given threshold. We
first show that the problem at hand is hard to approximate within a constant
factor that is not dependent on the properties of the payment function.
However, for given topological and analytical properties of the instance, we
construct two greedy algorithms, respectively called PPCGreedy and
PPCGreedyIter, and establish theoretical bounds on their performance w.r.t. the
optimal solution. Finally, we evaluate our methods using a realistic crowd
sensing testbed.Comment: Longer version of AAAI'18 pape
'Leaning against an open door' : ideology and the cyclicality of public expenditure
When is government expenditure likely to be procyclical? While economists tend to anticipate counter-cyclical expenditure, recent studies report procyclical expenditure. This paper explores the impact of political ideology on the cyclicality of government expenditure. Predictions are tested with reference to government expenditure in the USA between 1950 and 2008. The likelihood of procyclical expenditure increases if groups that press for increased public expenditure are â... leaning against an open doorâ
Banking Passivity And Regulatory Failure In Emerging Markets: Theory And Evidence From The Czech Republic.
We present a model of bank passivity and regulatory failure. Banks with low equity positions have more incentives to be passive in liquidating bad loans. We show that they tend to hide distress from regulatory authorities and are ready to offer a higher rate of interest in order to attract deposits compared to banks that are not in distress. Therefore, higher deposit rates may act as an early warning signal of bank failure. We provide empirical evidence that the balance sheet information collected by the Czech National Bank is not a better predictor of bank failure than higher deposit rates. This confirms the importance of asymmetric information between banks and the regulator and suggests the usefulness of looking at deposit rate differentials as early signals of distress in emerging market economies where banks' equity positions are often low.http://deepblue.lib.umich.edu/bitstream/2027.42/39808/3/wp424.pd
Price dynamics, informational efficiency and wealth distribution in continuous double auction markets
This paper studies the properties of the continuous double auction trading mechanishm using an artificial market populated by heterogeneous computational agents. In particular, we investigate how changes in the population of traders and in market microstructure characteristics affect price dynamics, information dissemination and distribution of wealth across agents. In our computer simulated market only a small fraction of the population observe the risky asset's fundamental value with noise, while the rest of agents try to forecast the asset's price from past transaction data. In contrast to other artificial markets, we assume that the risky asset pays no dividend, so agents cannot learn from past transaction prices and subsequent dividend payments. We find that private information can effectively disseminate in the market unless market regulation prevents informed investors from short selling or borrowing the asset, and these investors do not constitute a critical mass. In such case, not only are markets less efficient informationally, but may even experience crashes and bubbles. Finally, increased informational efficiency has a negative impact on informed agents' trading profits and a positive impact on artificial intelligent agents' profits
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