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

Decentralization, Interdependence and Performance Measurement System Design:Sequences and Priorities

We investigate the determinants of decentralization and performance measurement choices in multidivisional firms.We extend the research on the economics of organizational design choices by examining the impact of two important determinants of those choices, namely, subunit interdependencies and knowledge transfer costs.We test our predictions with a simultaneous equation model that captures the endogenous choices relating to the level of decentralization and the use of alternative subunit performance measures using data collected from 78 business units.Our findings are generally consistent with our predictions.

Deceptive body movements reverse spatial cueing in soccer

This article has been made available through the Brunel Open Access Publishing Fund.The purpose of the experiments was to analyse the spatial cueing effects of the movements of soccer players executing normal and deceptive (step-over) turns with the ball. Stimuli comprised normal resolution or point-light video clips of soccer players dribbling a football towards the observer then turning right or left with the ball. Clips were curtailed before or on the turn (-160, -80, 0 or +80 ms) to examine the time course of direction prediction and spatial cueing effects. Participants were divided into higher-skilled (HS) and lower-skilled (LS) groups according to soccer experience. In experiment 1, accuracy on full video clips was higher than on point-light but results followed the same overall pattern. Both HS and LS groups correctly identified direction on normal moves at all occlusion levels. For deceptive moves, LS participants were significantly worse than chance and HS participants were somewhat more accurate but nevertheless substantially impaired. In experiment 2, point-light clips were used to cue a lateral target. HS and LS groups showed faster reaction times to targets that were congruent with the direction of normal turns, and to targets incongruent with the direction of deceptive turns. The reversed cueing by deceptive moves coincided with earlier kinematic events than cueing by normal moves. It is concluded that the body kinematics of soccer players generate spatial cueing effects when viewed from an opponent's perspective. This could create a reaction time advantage when anticipating the direction of a normal move. A deceptive move is designed to turn this cueing advantage into a disadvantage. Acting on the basis of advance information, the presence of deceptive moves primes responses in the wrong direction, which may be only partly mitigated by delaying a response until veridical cues emerge

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

Changing Bases: Multistage Optimization for Matroids and Matchings

This paper is motivated by the fact that many systems need to be maintained continually while the underlying costs change over time. The challenge is to continually maintain near-optimal solutions to the underlying optimization problems, without creating too much churn in the solution itself. We model this as a multistage combinatorial optimization problem where the input is a sequence of cost functions (one for each time step); while we can change the solution from step to step, we incur an additional cost for every such change. We study the multistage matroid maintenance problem, where we need to maintain a base of a matroid in each time step under the changing cost functions and acquisition costs for adding new elements. The online version of this problem generalizes online paging. E.g., given a graph, we need to maintain a spanning tree $T_t$ at each step: we pay $c_t(T_t)$ for the cost of the tree at time $t$, and also $| T_t\setminus T_{t-1} |$ for the number of edges changed at this step. Our main result is an $O(\log m \log r)$-approximation, where $m$ is the number of elements/edges and $r$ is the rank of the matroid. We also give an $O(\log m)$ approximation for the offline version of the problem. These bounds hold when the acquisition costs are non-uniform, in which caseboth these results are the best possible unless P=NP. We also study the perfect matching version of the problem, where we must maintain a perfect matching at each step under changing cost functions and costs for adding new elements. Surprisingly, the hardness drastically increases: for any constant $\epsilon>0$, there is no $O(n^{1-\epsilon})$-approximation to the multistage matching maintenance problem, even in the offline case

Implications for telehealth for accessing education in rural areas: children with a severe chronic disease.

Children and their families who live in rural and remote areas are often disadvantaged by distance. In healthcare, this can be especially problematic. Children can suffer from a range of chronic conditions, e.g. diabetes, asthma, cardiac conditions, cystic fibrosis and others. In Australia, health services for children and families with such conditions are centred in specialist children’s hospitals in the capital cities in each state, but the burden of health care often falls to the parents and the children themselves. While rural health services do a wonderful job providing health care for these children, it is very rare to find specialist services in any rural situation. For example, children with cystic fibrosis who live in remote parts of Queensland attend specialist clinics in their local hospital twice or three times a year for routine check-ups, when the cystic fibrosis team of nurses, doctors and allied health staff from the children’s hospital in Brisbane travels to rural areas. If children become acutely ill, they might be able to be treated in the local hospital if they are not too sick, or they could be taken to the children’s hospital in Brisbane by their parents. If they are having a serious exacerbation of the illness, they will be transported there by aircraft and ambulance. Any child being sick is stressful for the family, regardless of where they live. However, if families live thousands of kilometres from the main treatment centres, scenarios described above can be common, with subsequent family disruption and emotional, social and economic costs. Telehealth is being installed in many rural and remote health services, thereby allowing country families the benefit of specialist consultation and care. However, governments and health departments are only slowly engaging with such technology. This paper presents findings of a study in Far North Queensland which examined how care was delivered to rural and remote families with children with cystic fibrosis, and how they cope. It will discuss how telehealth could improve care to such families and pose questions about why this is so slow in being implemented in Australia