29,327 research outputs found
A Collaborative Mechanism for Crowdsourcing Prediction Problems
Machine Learning competitions such as the Netflix Prize have proven
reasonably successful as a method of "crowdsourcing" prediction tasks. But
these competitions have a number of weaknesses, particularly in the incentive
structure they create for the participants. We propose a new approach, called a
Crowdsourced Learning Mechanism, in which participants collaboratively "learn"
a hypothesis for a given prediction task. The approach draws heavily from the
concept of a prediction market, where traders bet on the likelihood of a future
event. In our framework, the mechanism continues to publish the current
hypothesis, and participants can modify this hypothesis by wagering on an
update. The critical incentive property is that a participant will profit an
amount that scales according to how much her update improves performance on a
released test set.Comment: Full version of the extended abstract which appeared in NIPS 201
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
Privacy and Truthful Equilibrium Selection for Aggregative Games
We study a very general class of games --- multi-dimensional aggregative
games --- which in particular generalize both anonymous games and weighted
congestion games. For any such game that is also large, we solve the
equilibrium selection problem in a strong sense. In particular, we give an
efficient weak mediator: a mechanism which has only the power to listen to
reported types and provide non-binding suggested actions, such that (a) it is
an asymptotic Nash equilibrium for every player to truthfully report their type
to the mediator, and then follow its suggested action; and (b) that when
players do so, they end up coordinating on a particular asymptotic pure
strategy Nash equilibrium of the induced complete information game. In fact,
truthful reporting is an ex-post Nash equilibrium of the mediated game, so our
solution applies even in settings of incomplete information, and even when
player types are arbitrary or worst-case (i.e. not drawn from a common prior).
We achieve this by giving an efficient differentially private algorithm for
computing a Nash equilibrium in such games. The rates of convergence to
equilibrium in all of our results are inverse polynomial in the number of
players . We also apply our main results to a multi-dimensional market game.
Our results can be viewed as giving, for a rich class of games, a more robust
version of the Revelation Principle, in that we work with weaker informational
assumptions (no common prior), yet provide a stronger solution concept (ex-post
Nash versus Bayes Nash equilibrium). In comparison to previous work, our main
conceptual contribution is showing that weak mediators are a game theoretic
object that exist in a wide variety of games -- previously, they were only
known to exist in traffic routing games
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
Planning effectual growth: a study of effectuations and causation in nascent firms
Two main contrasting approaches are used in the entrepreneurship literature to explain how new ventures strategize: causal/planned strategies and effectual/emergent strategies. In this study, we explore the use of these strategies within micro and small firms. Our results show that larger companies typically used more planned strategies while simultaneously relying on effectual mechanisms. We observe that companies operating in known markets, anchoring their business ideas on experience and having a strong growth intention grow larger. This suggests that causal and effectual mechanisms can co-exist and lead to growth when combined. Theoretical and practical implications of these findings are discussed
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