An important problem with online communities in general, and online rating systems in particular, is uncooperative behavior: lack of user participation, dishonest contributions. This may be due to an incentive structure akin to a Prisoners' Dilemma (PD). We show that introducing an explicit social network to PD games fosters cooperative behavior, and use this insight to design a new aggregation technique for online rating systems. Using a dataset of ratings from Yelp, we show that our aggregation technique outperforms Yelp's proprietary filter, as well as baseline techniques from recommender systems.
Many data sharing systems are open to arbitrary users on the Internet, who are independent and self-interested agents. Therefore, in addition to traditional design goals such as technical performance, data sharing systems should be designed to best support the strategic interactions of these agents. Our research hypothesis is that designs that maximize the participants' autonomy can produce useful data sharing systems. We apply this design principle to both the system architecture and the functional design of a data sharing system, and study the resulting class of systems, which we call Decentralized Social Data Sharing ((DS)²) systems. We formally define this class of systems and provide a reference implementation and an example application: a distributed wiki system called P2Pedia. P2Pedia implements a decentralized collaboration model, where the users are not required to reach a consensus, and instead benefit from being exposed to multiple viewpoints. We demonstrate the value of this collaboration model through an extensive user study. Allowing the users to autonomously control their data prevents the system architecture from being optimized for efficient query processing. We show that Regular Path Queries, a useful class of graph queries, can still be processed on the shared data: although in the worst case such queries are intractable, we propose a cost estimation technique to identify tractable queries from partial knowledge of the data. Through simulation, we also show that the users' control over network connections allows them to self-organize and interact with other users with whom their interests are best aligned. This may result in less data being available, and we study cases where this is in fact demonstrably beneficial to the users, as the available data to each user is the most relevant to them. This suggests that querying this reduced collection of shared data may lead to more tractable query processing without necessarily reducing the users' utility.
In today's interconnected world, people interact to a unprecedented degree through the use of digital platforms and services, forming complex 'social machines'. These are now homes to autonomous agents as well as people, providing an open space where human and computational intelligence can mingle---a new frontier for distributed agent systems. However, participants typically have limited autonomy to define and shape the machines they are part of. In this paper, we envision a future where individuals are able to develop their own Social Machines, enabling them to interact in a trustworthy, decentralized way. To make this possible, development methods and tools must see their barriers-to-entry dramatically lowered. People should be able to specify the agent roles and interaction patterns in an intuitive, visual way, analyse and test their designs and deploy them as easy to use systems. We argue that this is a challenging but realistic goal, which should be tackled by navigating the trade-off between the accessibility of the design methods --primarily the modelling formalisms-- and their expressive power. We support our arguments by drawing ideas from different research areas including electronic institutions, agent-based simulation, process modelling, formal verification, and model-driven engineering.
Regular Path Queries (RPQs) are a type of graph query where answers are pairs of nodes connected by a sequence of edges matching a regular expression. We study the techniques to process such queries on a distributed graph of data. While many techniques assume the location of each data element (node or edge) is known, when the components of the distributed system are autonomous, the data will be arbitrarily distributed. As the different query processing strategies are equivalently costly in the worst case, we isolate query-dependent cost factors and present a method to choose between strategies, using new query cost estimation techniques. We evaluate our techniques using meaningful queries on biomedical data.
Purpose The purpose of this paper is to report on empirical work conducted to open up algorithmic interpretability and transparency. In recent years, significant concerns have arisen regarding the increasing pervasiveness of algorithms and the impact of automated decision-making in our lives. Particularly problematic is the lack of transparency surrounding the development of these algorithmic systems and their use. It is often suggested that to make algorithms more fair, they should be made more transparent, but exactly how this can be achieved remains unclear. Design/methodology/approach An empirical study was conducted to begin unpacking issues around algorithmic interpretability and transparency. The study involved discussion-based experiments centred around a limited resource allocation scenario which required participants to select their most and least preferred algorithms in a particular context. In addition to collecting quantitative data about preferences, qualitative data captured participants’ expressed reasoning behind their selections. Findings Even when provided with the same information about the scenario, participants made different algorithm preference selections and rationalised their selections differently. The study results revealed diversity in participant responses but consistency in the emphasis they placed on normative concerns and the importance of context when accounting for their selections. The issues raised by participants as important to their selections resonate closely with values that have come to the fore in current debates over algorithm prevalence. Originality/value This work developed a novel empirical approach that demonstrates the value in pursuing algorithmic interpretability and transparency while also highlighting the complexities surrounding their accomplishment.
We report our experience using a peer-to-peer (P2P) wiki system for academic writing tutorials. Our wiki system supports a non-traditional collaboration model, where each participant maintains their own version of the documents.
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