exhibit considerable selective or performance effects on workers, corroborated in practice by the fact that most traditional businesses employ incentive schemes [Feh13, Ch. 1].
2.2
Related Work
Most related work in the area of rewarding and incentives originates from the economics, organizational science, psychology, and applied research, mostly for military purposes. It can be used to classify and substantiate the basic rewarding approaches, and expected outcomes, and to simulate the responses to our incentive strategies. There is only a small number of computer science papers that threat the topics of incentives and rewarding, usually within particular application contexts (e.g. peer-to-peer networks, agent-based systems). However, to the best of our knowledge, no other computer science paper threats the topic in a comprehensive manner. In fact, most papers completely disregard the existing theoretical foundations of incentives, and are concerned with solving only the particular problem, as we will show in the rest of this section. The work [Vas12] is a notable exception, discussing incentives designed to motivate participation in different social computing platforms and relating them to the leading behavioral theories, and presenting a vision for the future developments in this research area. In [SHY+ 08a] the aim is to maximize p2p content sharing. Therefore, they define roles of (content) forwarders and receivers. Forwarder gets a reward when the receiver reacts in some way to the content being forwarded. They then define the prices of forwarding, and receiving actions and assign the incentives based on that. Many other papers similarly identify certain behavioral patterns and develop particular solutions to prevent unwanted behavior or enhance existing algorithms to optimize certain metrics ([Kau11]). The paper [FGP+ 09] discusses ways of modeling and implementing adaptable agentbased systems. Each agent can be modified by adding or removing ”modules” that the agent consists of. Cause for agent adaptability is usually a role change within an organization. In [Lit10] the authors are trying to determine quality of work achieved when a task is done iteratively compared to when it is done in parallel. The difference is that in iterative processes (when applicable), workers are shown previous attempts by other workers, which can influence their work in positive or negative way. They conduct experiments with real workers on Amazon Mechanical Turk, and prove statistically that, when applicable, the iterative approach yields better (more accurate) results. The quality of the work in their experiments is quantifiable, or voted by the crowd. This is an important finding, since it justifies the choice of iterative execution model that we adopt. In [MW09] two basic findings seem robust, and can be used as general conclusions when modeling behavior of contributors: “First, that paying subjects elicited higher output than not paying them (where increasing their pay rate also yielded higher output); and second, that in contrast to the quantity of work done, paying subjects did not affect their accuracy. Although surprising, this latter result may be related to an “anchoring effect” in that subjects’ perception of the value of their work was strongly correlated with their actual pay rate.” 15