Increasingly, organisations flexibly outsource work on a temporary basis to a global audience of workers. This so-called crowdsourcing has been applied successfully to a range of tasks, from translating text and annotating images, to collecting information during crisis situations and hiring skilled workers to build complex software. While traditionally these tasks have been small and could be completed by non-professionals, organisations are now starting to crowdsource larger, more complex tasks to experts in their respective fields. These tasks include, for example, software development and testing, web design and product marketing. While this emerging expert crowdsourcing offers flexibility and potentially lower costs, it also raises new challenges, as workers can be highly heterogeneous, both in their costs and in the quality of the work they produce. Specifically, the utility of each outsourced task is uncertain and can vary significantly between distinct workers and even between subsequent tasks assigned to the same worker. Furthermore, in realistic settings, workers have limits on the amount of work they can perform and the employer will have a fixed budget for paying workers. Given this uncertainty and the relevant constraints, the objective of the employer is to assign tasks to workers in order to maximise the overall utility achieved. To formalise this expert crowdsourcing problem, we introduce a novel multi-armed bandit (MAB) model, the bounded MAB. Furthermore, we develop an algorithm to solve it efficiently, called bounded ε-first, which proceeds in two stages: exploration and exploitation. During exploration, it first uses εB of its total budget B to learn estimates of the workers' quality characteristics. Then, during exploitation, it uses the remaining (1 − ε)B to maximise the total utility based on those estimates. Using this technique allows us to derive an O (B 2 3 ) upper bound on its performance regret (i.e., the expected difference in utility between our algorithm and the optimum), which means that as the budget B increases, the regret tends to 0. In addition to this theoretical advance, we apply our algorithm to real-world data from oDesk, a prominent expert crowdsourcing site. Using data from real projects, including historic project budgets, expert costs and quality ratings, we show that our algorithm outperforms existing crowdsourcing methods by up to 300%, while achieving up to 95% of a hypothetical optimum with full information.

The problem of promoting the evolution of cooperative behaviour within populations of self-regarding individuals has been intensively investigated across diverse fields of behavioural, social and computational sciences. In most studies, cooperation is assumed to emerge from the combined actions of participating individuals within the populations, without taking into account the possibility of external interference and how it can be performed in a cost-efficient way. Here, we bridge this gap by studying a cost-efficient interference model based on evolutionary game theory, where an exogenous decision-maker aims to ensure high levels of cooperation from a population of individuals playing the one-shot Prisoner’s Dilemma, at a minimal cost. We derive analytical conditions for which an interference scheme or strategy can guarantee a given level of cooperation while at the same time minimising the total cost of investment (for rewarding cooperative behaviours), and show that the results are highly sensitive to the intensity of selection by interference. Interestingly, we show that a simple class of interference that makes investment decisions based on the population composition can lead to significantly more cost-efficient outcomes than standard institutional incentive strategies, especially in the case of weak selection.

In this paper, we address the problem of predicting the usage of home appliances where a key challenge is to model the everyday routine of homeowners and the inter-dependency between the use of different appliances. To this end, we propose an agent based prediction algorithm that captures the everyday habits by exploiting their periodic features. We demonstrate that our approach outperforms existing methods by up to 40% in experiments based on real-world data from a prominent database of home energy usage.

Abstract. We study the existence of pure strategy Nash equilibria (PSNE) in integer-splittable weighted congestion games (ISWCGs), where agents can strategically assign different amounts of demand to different resources, but must distribute this demand in fixed-size parts. Such scenarios arise in a wide range of application domains, including job scheduling and network routing, where agents have to allocate multiple tasks and can assign a number of tasks to a particular selected resource. Specifically, in an ISWCG, an agent has a certain total demand (aka weight) that it needs to satisfy, and can do so by requesting one or more integer units of each resource from an element of a given collection of feasible subsets.1 Each resource is associated with a unit-cost function of its level of congestion; as such, the cost to an agent for using a particular resource is the product of the resource unit-cost and the number of units the agent requests. While general ISWCGs do not admit PSNE (Rosenthal, 1973b), the restricted subclass of these games with linear unit-cost functions has been shown to possess a potential function (Meyers, 2006), and hence, PSNE. However, the linearity of costs may not be necessary for the existence of equilibria in pure strategies. Thus, in this paper we prove that PSNE always exist for a larger class of convex and monotonically increasing unit-costs. On the other hand, our result is accompanied by a limiting asumption on the structure of agents' strategy sets: specifically, each agent is associated with its set of accessible resources, and can distribute its demand across any subset of these resources. Importantly, we show that neither monotonicity nor convexity on its own guarantees this result. Moreover, we give a counterexample with monotone and semi-convex cost functions, thus distinguishing ISWCGs from the class of infinitely-splittable congestion games for which the conditions of monotonicity and semi-convexity have been shown to be sufficient for PSNE existence (Rosen, 1965). Furthermore, we demonstrate that the finite improvement path property (FIP) does not hold for convex increasing ISWCGs. Thus, in contrast to the case with linear costs, a potential function argument cannot be used to prove our result. Instead, we provide a procedure that converges to an equilibrium from an arbitrary initial strategy profile, and in doing so show that ISWCGs with convex increasing unit-cost functions are weakly acyclic.

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