Causes of variation in human cooperative behaviour

This thesis investigates variation in human cooperative behaviour in naturally occurring contexts. I critically assess the prevailing consensus on human cooperation derived from laboratory games (such as the dictator and public goods games), by identifying real life analogues and conducting extensive field observation and experiments. My second chapter investigates the importance of context on social behaviour by taking a commonly used laboratory game, the dictator game, and studying analogous behaviour, giving to mendicants in the street. I conclude that individuals cooperate less in the wild than they do in the laboratory and that monetary pay-offs are important in cooperative decision-making. My third chapter examines how social cues influence peoples’ likelihood of giving to mendicants. I conclude that increased group size and crowd density negatively affect donation behaviour. My fourth chapter investigates dog fouling in public parks to understand the causes of variation in cheating in a naturally occurring public goods game. I conclude that despite evidence that a social game is being played, the cues that influences decisions are unclear, and behaviour may depend on local social norms. My fifth chapter investigates social influences on red light jumping by cyclists at pedestrian crossings. I find that the probability of cheating is higher with fewer observers and when other cyclists also cheat

Distributed Reinforcement Learning for Network Intrusion Response

The increasing adoption of technologies and the exponential growth of networks has made the area of information technology an integral part of our lives, where network security plays a vital role. One of the most serious threats in the current Internet is posed by distributed denial of service (DDoS) attacks, which target the availability of the victim system. Such an attack is designed to exhaust a server's resources or congest a network's infrastructure, and therefore renders the victim incapable of providing services to its legitimate users or customers. To tackle the distributed nature of these attacks, a distributed and coordinated defence mechanism is necessary, where many defensive nodes, across different locations cooperate in order to stop or reduce the flood. This thesis investigates the applicability of distributed reinforcement learning to intrusion response, specifically, DDoS response. We propose a novel approach to respond to DDoS attacks called Multiagent Router Throttling. Multiagent Router Throttling provides an agent-based distributed response to the DDoS problem, where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. One of the novel characteristics of the proposed approach is that it has a decentralised architecture and provides a decentralised coordinated response to the DDoS problem, thus being resilient to the attacks themselves. Scalability constitutes a critical aspect of a defence system since a non-scalable mechanism will never be considered, let alone adopted, for wide deployment by a company or organisation. We propose Coordinated Team Learning (CTL) which is a novel design to the original Multiagent Router Throttling approach based on the divide-and-conquer paradigm, that uses task decomposition and coordinated team rewards. To better scale-up CTL is combined with a form of reward shaping. The scalability of the proposed system is successfully demonstrated in experiments involving up to 1000 reinforcement learning agents. The significant improvements on scalability and learning speed lay the foundations for a potential real-world deployment

Incentive-driven QoS in peer-to-peer overlays

A well known problem in peer-to-peer overlays is that no single entity has control over the software, hardware and configuration of peers. Thus, each peer can selfishly adapt its behaviour to maximise its benefit from the overlay. This thesis is concerned with the modelling and design of incentive mechanisms for QoS-overlays: resource allocation protocols that provide strategic peers with participation incentives, while at the same time optimising the performance of the peer-to-peer distribution overlay. The contributions of this thesis are as follows. First, we present PledgeRoute, a novel contribution accounting system that can be used, along with a set of reciprocity policies, as an incentive mechanism to encourage peers to contribute resources even when users are not actively consuming overlay services. This mechanism uses a decentralised credit network, is resilient to sybil attacks, and allows peers to achieve time and space deferred contribution reciprocity. Then, we present a novel, QoS-aware resource allocation model based on Vickrey auctions that uses PledgeRoute as a substrate. It acts as an incentive mechanism by providing efficient overlay construction, while at the same time allocating increasing service quality to those peers that contribute more to the network. The model is then applied to lagsensitive chunk swarming, and some of its properties are explored for different peer delay distributions. When considering QoS overlays deployed over the best-effort Internet, the quality received by a client cannot be adjudicated completely to either its serving peer or the intervening network between them. By drawing parallels between this situation and well-known hidden action situations in microeconomics, we propose a novel scheme to ensure adherence to advertised QoS levels. We then apply it to delay-sensitive chunk distribution overlays and present the optimal contract payments required, along with a method for QoS contract enforcement through reciprocative strategies. We also present a probabilistic model for application-layer delay as a function of the prevailing network conditions. Finally, we address the incentives of managed overlays, and the prediction of their behaviour. We propose two novel models of multihoming managed overlay incentives in which overlays can freely allocate their traffic flows between different ISPs. One is obtained by optimising an overlay utility function with desired properties, while the other is designed for data-driven least-squares fitting of the cross elasticity of demand. This last model is then used to solve for ISP profit maximisation

Evolution through reputation: noise-resistant selection in evolutionary multi-agent systems

Little attention has been paid, in depth, to the relationship between fitness evaluation in evolutionary algorithms and reputation mechanisms in multi-agent systems, but if these could be related it opens the way for implementation of distributed evolutionary systems via multi-agent architectures. Our investigation concentrates on the effectiveness with which social selection, in the form of reputation, can replace direct fitness observation as the selection bias in an evolutionary multi-agent system. We do this in two stages: In the first, we implement a peer-to-peer, adaptive Genetic Algorithm (GA), in which agents act as individual GAs that, in turn, evolve dynamically themselves in real-time, using the traditional evolutionary operators of fitness-based selection, crossover and mutation. In the second stage, we replace the fitness-based selection operator with a reputation-based one, in which agents choose their mates based on the collective past experiences of themselves and their peers. Our investigation shows that this simple model of distributed reputation can be successful as the evolutionary drive in such a system, exhibiting practically identical performance and scalability to direct fitness observation. Further, we discuss the effect of noise (in the form of “defective” agents) in both models. We show that the reputation-based model is significantly better at identifying the defective agents, thus showing an increased level of resistance to noise

Influencing attitudes, changing behaviours and embedding a pro-sustainability mindset in the workplace: An innovation diffusion approach to persuasive communications.

Although several sustainability implementation frameworks have been proposed, researchers have not yet proposed theories or models to help organisations speed up the rate of sustainability diffusion and narrow the gap between what is known and what is put into use. This study sought to fill this gap by proposing a sustainability diffusion model. The model was developed from an exhaustive review of the corresponding literature. It uses Rogers' (1962) diffusion of innovations theory and Ajzen's (1991) theory of planned behaviour as a theoretical foundation. The model was tested and its structural architecture was validated in three different sustainability contexts; namely, duplex printing in UK universities; sustainable computing in service-based businesses; and sustainability culture in UK universities. The primary data was analysed statistically using SPSS, and structural equation modelling (SEM) in particular was used to validate the structural architecture of the proposed model. The SEM results indicate that the structural architecture of the theory of planned behaviour is well-founded. All the hypotheses that underline the theory's paths were supported. In contrast, the structural architecture of the diffusion of innovations theory was weakly supported. Some of the paths were rejected in at least two occasions. For example, the relationship between pro-sustainability knowledge and attitude was neither statistically significant nor directional. Moreover, several components of the 'verified' model turned out to be statistically insignificant or were rejected altogether. These were knowledge, perceived self interest, perceived persuader legitimacy, perceived consequences, perceived argument quality, trialability and perceived source credibility. Accordingly, once these constructs were removed and the model was restructured in accordance with the results of SEM analysis, an entirely new version of the 'sustainability diffusion model' emerged (See Figure IX-2). The architecture of the new model suggests that in order to speed up the rate of sustainability diffusion, change agents must emphasise the relative advantage, compatibility, subjective norm and the urgency of the pro-sustainability initiative under implementation and de-emphasise any complexities or risks associated with its operationalisation. Unexpectedly, the new version of the proposed model relies more on Ajzen's (1991) theory of planned behaviour as a theoretical foundation than on Rogers' (1983) innovation-decision process model. In other words, the new model maintained almost all the features of the theory of planned behaviour, but it only absorbed some, but not all, of the components of Rogers' innovation-decision process model. Nevertheless, the new model maintained its holistic nature. It still takes into account both the person-specific and innovation-specific factors that influence the diffusion, adoption and actualisation of pro-sustainability behaviours/initiatives

Aversive reinforcement learning

We hypothesise that human aversive learning can be described algorithmically by Reinforcement Learning models. Our first experiment uses a second-order conditioning design to study sequential outcome prediction. We show that aversive prediction errors are expressed robustly in the ventral striatum, supporting the validity of temporal difference algorithms (as in reward learning), and suggesting a putative critical area for appetitive-aversive interactions. With this in mind, the second experiment explores the nature of pain relief, which as expounded in theories of motivational opponency, is rewarding. In a Pavlovian conditioning task with phasic relief of tonic noxious thermal stimulation, we show that both appetitive and aversive prediction errors are co-expressed in anatomically dissociable regions (in a mirror opponent pattern) and that striatal activity appears to reflect integrated appetitive-aversive processing. Next we designed a Pavlovian task in which cues predicted either financial gains, losses, or both, thereby forcing integration of both motivational streams. This showed anatomical dissociation of aversive and appetitive predictions along a posterior-anterior gradient within the striatum, respectively. Lastly, we studied aversive instrumental control (avoidance). We designed a simultaneous pain avoidance and financial reward learning task, in which subjects had to learn independently learn about each, and trade off aversive and appetitive predictions. We show that predictions for both converge on the medial head of caudate nucleus, suggesting that this is a critical site for appetitive-aversive integration in instrumental decision making. We also study also tested whether serotonin (5HT) modulates either phasic or tonic opponency using acute tryptophan depletion. Both behavioural and imaging data confirm the latter, in which it appears to mediate an average reward term, providing an aspiration level against which the benefits of exploration are judged. In summary, our data provide a basic computational and neuroanatomical framework for human aversive learning. We demonstrate the algorithmic and implementational validity of reinforcement learning models for both aversive prediction and control, illustrate the nature and neuroanatomy of appetitive-aversive integration, and discover the critical (and somewhat unexpected) central role for the striatum

Private and censorship-resistant communication over public networks

Society’s increasing reliance on digital communication networks is creating unprecedented opportunities for wholesale surveillance and censorship. This thesis investigates the use of public networks such as the Internet to build robust, private communication systems that can resist monitoring and attacks by powerful adversaries such as national governments. We sketch the design of a censorship-resistant communication system based on peer-to-peer Internet overlays in which the participants only communicate directly with people they know and trust. This ‘friend-to-friend’ approach protects the participants’ privacy, but it also presents two significant challenges. The first is that, as with any peer-to-peer overlay, the users of the system must collectively provide the resources necessary for its operation; some users might prefer to use the system without contributing resources equal to those they consume, and if many users do so, the system may not be able to survive. To address this challenge we present a new game theoretic model of the problem of encouraging cooperation between selfish actors under conditions of scarcity, and develop a strategy for the game that provides rational incentives for cooperation under a wide range of conditions. The second challenge is that the structure of a friend-to-friend overlay may reveal the users’ social relationships to an adversary monitoring the underlying network. To conceal their sensitive relationships from the adversary, the users must be able to communicate indirectly across the overlay in a way that resists monitoring and attacks by other participants. We address this second challenge by developing two new routing protocols that robustly deliver messages across networks with unknown topologies, without revealing the identities of the communication endpoints to intermediate nodes or vice versa. The protocols make use of a novel unforgeable acknowledgement mechanism that proves that a message has been delivered without identifying the source or destination of the message or the path by which it was delivered. One of the routing protocols is shown to be robust to attacks by malicious participants, while the other provides rational incentives for selfish participants to cooperate in forwarding messages

Analyses on tech-enhanced and anonymous Peer Discussion as well as anonymous Control Facilities for tech-enhanced Learning

An increasing number of university freshmen has been observable in absolute number as well as percentage of population over the last decade. However, at the same time the drop-out rate has increased significantly. While a drop in attendance could be observed at the same time, statistics show that young professionals consider only roughly thirty percent of their qualification to originate in their university education. Taking this into consideration with the before mentioned, one conclusion could be that students fail to see the importance of fundamental classes and choose to seek knowledge elsewhere, for example in free online courses. However, the so acquired knowledge is a non-attributable qualification. One solution to this problem must be to make on-site activities more attractive. A promising approach for raised attractiveness would be to support students in self-regulated learning processes, making them experience importance and value of own decisions based on realistic self-assessment and self-evaluation. At the same time, strict ex-cathedra teaching should be replaced by interactive forms of education, ideally activating on a meta-cognitive level. Particularly, as many students bring mobile communication devices into classes, this promising approach could be extended by utilising these mobile devices as second screens. That way, enhanced learning experiences can be provided. The basic idea is simple, namely to contribute to psychological concepts with the means of computer science. An example for this idea are audience response systems. There has been numerous research into these and related approaches for university readings, but other forms of education have not been sufficiently considered, for example tutorials. This technological aspect can be combined with recent didactics research and concepts like peer instruction or visible learning. Therefore, this dissertation presents an experimental approach at providing existing IT solutions for on-site tutorials, specifically tools for audience responses, evaluations, learning demand assessments, peer discussion, and virtual interactive whiteboards. These tools are provided under observation of anonymity and cognisant incidental utilisation. They provide insight into students\' motivation to attend classes, their motivation to utilise tools, and into their tool utilisation itself. Experimental findings are combined into an extensible system concept consisting of three major tool classes: anonymous peer discussion means, anonymous control facilities, and learning demand assessment. With the exception of the latter, promising findings in context of tutorials are presented, for example the reduction of audience response systems to an emergency brake, the versatility of (peer) discussion systems, or a demand for retroactive deanonymisation of contributions. The overall positive impact of tool utilisation on motivation to attend and perceived value of tutorials is discussed and supplemented by a positive impact on the final exams\' outcomes.:List of Definitions, Theorems and Proofs List of Figures List of Tables Introduction and Motivation Part I: Propaedeutics 1 Working Theses 1.1 Definitions 1.2 Context of Working Theses and Definitions 2 Existing Concepts 2.1 Psychology 2.1.1 Self-Regulation and self-regulated Learning 2.1.2 Peer Instruction, Peer Discussion 2.1.3 Learning Process Supervision: Learning Demand Assessment 2.1.4 Cognitive Activation 2.1.5 Note on Gamification 2.1.6 Note on Blended Learning 2.2 Computer Science 2.2.1 Learning Platforms 2.2.2 Audience Response Systems (ARS) 2.2.3 Virtual Interactive Whiteboard Systems (V-IWB) 2.2.4 Cognisant Incidential Utilisation (CIU) 2.3 Appraisal 3 Related Work 3.1 Visible Learning 3.2 auditorium 3.3 Auditorium Mobile Classroom Service 3.4 ARSnova and other Audience Response Systems 3.5 Google Classroom 3.6 StackOverflow 3.7 AwwApp Part II: Proceedings 4 Global Picture and Prototype 4.1 Global Picture 4.2 System Architecture 4.2.1 Anonymous Discussion Means 4.2.2 Anonymous Control Facilities 4.3 Implementation 4.3.1 The Prototype 5 Investigated Tools 5.1 Note on Methodology 5.2 Anonymity 5.2.1 Methodology 5.2.2 Visible Learning Effects 5.2.3 Assertion 5.2.4 Experiments 5.2.5 Results 5.2.6 Conclusions 5.3 Learning Demand Assessment 5.3.1 Methodology 5.3.2 Visible Learning Effects 5.3.3 Tool Description 5.3.4 Assertion 5.3.5 Experiments 5.3.6 Results 5.3.7 Conclusions 5.4 Peer Discussion System 5.4.1 Methodology 5.4.2 Visible Learning Effects 5.4.3 Tool Description 5.4.4 Assertion 5.4.5 Experiments 5.4.6 Results 5.4.7 Conclusions 5.5 Virtual Interactive Whiteboard 5.5.1 Methodology 5.5.2 Visible Learning Effects 5.5.3 Tool Description 5.5.4 Assertion 5.5.5 Experiments 5.5.6 Results 5.5.7 Conclusions 5.6 Audience Response System and Emergency Brake 5.6.1 Methodology 5.6.2 Visible Learning Effects 5.6.3 Tool Description 5.6.4 Assertion 5.6.5 Experiments 5.6.6 Results 5.6.7 Conclusions 5.7 Evaluation System 5.7.1 Methodology 5.7.2 Visible Learning Effects 5.7.3 Tool Description 5.7.4 Assertion 5.7.5 Experiments 5.7.6 Results and Conclusion 6 Exam Outcome 7 Utilisation and Motivation 7.1 Prototype Utilisation 7.2 Motivational Aspects Part III: Appraisal 8 Lessons learned 9 Discussion 9.1 Working Theses’ Validity 9.2 Research Community: Impact and Outlook 9.2.1 Significance to Learning Psychology 9.3 Possible Extension of existing Solutions 10 Conclusion 10.1 Summary of scientific Contributions 10.2 Future Work Part IV: Appendix A Experimental Arrangement B Questionnaires B.1 Platform Feedback Sheet B.1.1 Original PFS in 2014 B.1.2 Original PFS in 2015 B.2 Minute Paper B.3 Motivation and Utilisation Questionnaires B.3.1 Motivation 2013 and 2014 B.3.2 Motivation 2015 B.3.3 Utilisation 2014 B.3.4 Utilisation 2015, Rev. I B.3.5 Utilisation 2015, Rev. II C References C.1 Auxiliary Means D Publications D.1 Original Research Contributions D.2 Student Theses E Glossary F Index G Milestones AcknowledgementsÜber die vergangene Dekade ist eine zunehmende Zahl Studienanfänger beobachtbar, sowohl in der absoluten Anzahl, als auch im Bevölkerungsanteil. Demgegenüber steht aber eine überproportional hohe Steigerung der Abbruchquote. Während gleichzeitig die Anwesenheit in universitären Lehrveranstaltungen sinkt, zeigen Statistiken, dass nur etwa ein Drittel der Berufseinsteiger die Grundlagen ihrer Qualifikation im Studium sieht. Daraus könnte man ableiten, dass Studierende den Wert und die Bedeutung universitärer Ausbildung unterschätzen und stattdessen Wissen in anderen Quellen suchen, beispielsweise unentgeltlichen Online-Angeboten. Das auf diese Art angeeignete Wissen stellt aber eine formell nicht nachweise Qualifikation dar. Ein Weg aus diesem Dilemma muss die Steigerung der Attraktivität der universitären Lehrveranstaltungen sein. Ein vielversprechender Ansatz ist die Unterstützung der Studierenden im selbst-regulierten Lernen, wodurch sie die Wichtigkeit und den Wert eigener Entscheidung(sfindungsprozesse) auf Basis realistischer Selbsteinschätzung und Selbstevaluation erlernen. Gleichzeitig sollte Frontalunterricht durch interaktive Lehrformen ersetzt werden, idealerweise durch Aktivierung auf meta-kognitiver Ebene. Dies ist vielversprechend insbesondere, weil viele Studierende ihre eigenen mobilen Endgeräte in Lehrveranstaltungen bringen. Diese Geräte können als Second Screen für die neuen Lehrkonzepte verwendet werden. Auf diese Art kann dann eine verbesserte Lernerfahrung vermittelt werden. Die Grundidee ist simpel, nämlich in der Psychologie bewährte Didaktik-Konzepte durch die Mittel der Informatik zu unterstützen. Ein Beispiel dafür sind Audience Response Systeme, die hinlänglich im Rahmen von Vorlesungen untersucht worden sind. Andere Lehrformen wurden dabei jedoch unzureichend berücksichtigt, beispielsweise Tutorien. Ähnliche Überlegungen gelten natürlich auch für bewährte didaktische Konzepte wie Peer Instruction oder Betrachtungen in Form von Visible Learning. Deshalb präsentiert diese Dissertation einen experimentellen Ansatz, informationstechnische Lösungen für vor-Ort-Übungen anzubieten, nämlich Werkzeuge für Audience Response Systeme, Evaluationen, Lernbedarfsermittlung, Peer Discussion, sowie virtuelle interaktive Whiteboards. Die genannten Werkzeuge wurden unter Beachtung von Anonymitäts- und Beiläufigkeitsaspekten bereitgestellt. Sie erlauben einen Einblick in die Motivation der Studierenden Tutorien zu besuchen und die Werkzeuge zu nutzen, sowie ihr Nutzungsverhalten selbst. Die experimentellen Ergebnisse werden in ein erweiterbares Systemkonzept kombiniert, das drei Werkzeugklassen unterstützt: anonyme Peer Discussion, anonyme Kontrollwerkzeuge und Lernbedarfsermittlung. Für die ersten beiden Klassen liegen vielversprechende Ergebnisse vor, beispielsweise die notwendige Reduktion des Audience Response Systems auf eine Art Notbremse, die Vielseitigkeit von (Peer-)Discussion-Systemen, oder aber auch der Bedarf für eine retroaktive Deanonymisierung von initial anonymen Beiträgen. Der allgemein positive Einfluss der Werkzeugnutzung auf die Motivation an Tutorien teilzunehmen sowie den wahrgenommenen Wert der Tutorien werden abschließend diskutiert und durch verbesserte Abschlussklausurergebnisse untermauert.:List of Definitions, Theorems and Proofs List of Figures List of Tables Introduction and Motivation Part I: Propaedeutics 1 Working Theses 1.1 Definitions 1.2 Context of Working Theses and Definitions 2 Existing Concepts 2.1 Psychology 2.1.1 Self-Regulation and self-regulated Learning 2.1.2 Peer Instruction, Peer Discussion 2.1.3 Learning Process Supervision: Learning Demand Assessment 2.1.4 Cognitive Activation 2.1.5 Note on Gamification 2.1.6 Note on Blended Learning 2.2 Computer Science 2.2.1 Learning Platforms 2.2.2 Audience Response Systems (ARS) 2.2.3 Virtual Interactive Whiteboard Systems (V-IWB) 2.2.4 Cognisant Incidential Utilisation (CIU) 2.3 Appraisal 3 Related Work 3.1 Visible Learning 3.2 auditorium 3.3 Auditorium Mobile Classroom Service 3.4 ARSnova and other Audience Response Systems 3.5 Google Classroom 3.6 StackOverflow 3.7 AwwApp Part II: Proceedings 4 Global Picture and Prototype 4.1 Global Picture 4.2 System Architecture 4.2.1 Anonymous Discussion Means 4.2.2 Anonymous Control Facilities 4.3 Implementation 4.3.1 The Prototype 5 Investigated Tools 5.1 Note on Methodology 5.2 Anonymity 5.2.1 Methodology 5.2.2 Visible Learning Effects 5.2.3 Assertion 5.2.4 Experiments 5.2.5 Results 5.2.6 Conclusions 5.3 Learning Demand Assessment 5.3.1 Methodology 5.3.2 Visible Learning Effects 5.3.3 Tool Description 5.3.4 Assertion 5.3.5 Experiments 5.3.6 Results 5.3.7 Conclusions 5.4 Peer Discussion System 5.4.1 Methodology 5.4.2 Visible Learning Effects 5.4.3 Tool Description 5.4.4 Assertion 5.4.5 Experiments 5.4.6 Results 5.4.7 Conclusions 5.5 Virtual Interactive Whiteboard 5.5.1 Methodology 5.5.2 Visible Learning Effects 5.5.3 Tool Description 5.5.4 Assertion 5.5.5 Experiments 5.5.6 Results 5.5.7 Conclusions 5.6 Audience Response System and Emergency Brake 5.6.1 Methodology 5.6.2 Visible Learning Effects 5.6.3 Tool Description 5.6.4 Assertion 5.6.5 Experiments 5.6.6 Results 5.6.7 Conclusions 5.7 Evaluation System 5.7.1 Methodology 5.7.2 Visible Learning Effects 5.7.3 Tool Description 5.7.4 Assertion 5.7.5 Experiments 5.7.6 Results and Conclusion 6 Exam Outcome 7 Utilisation and Motivation 7.1 Prototype Utilisation 7.2 Motivational Aspects Part III: Appraisal 8 Lessons learned 9 Discussion 9.1 Working Theses’ Validity 9.2 Research Community: Impact and Outlook 9.2.1 Significance to Learning Psychology 9.3 Possible Extension of existing Solutions 10 Conclusion 10.1 Summary of scientific Contributions 10.2 Future Work Part IV: Appendix A Experimental Arrangement B Questionnaires B.1 Platform Feedback Sheet B.1.1 Original PFS in 2014 B.1.2 Original PFS in 2015 B.2 Minute Paper B.3 Motivation and Utilisation Questionnaires B.3.1 Motivation 2013 and 2014 B.3.2 Motivation 2015 B.3.3 Utilisation 2014 B.3.4 Utilisation 2015, Rev. I B.3.5 Utilisation 2015, Rev. II C References C.1 Auxiliary Means D Publications D.1 Original Research Contributions D.2 Student Theses E Glossary F Index G Milestones Acknowledgement

Multi-agent based simulation of self-governing knowledge commons

The potential of user-generated sensor data for participatory sensing has motivated the formation of organisations focused on the exploitation of collected information and associated knowledge. Given the power and value of both the raw data and the derived knowledge, we advocate an open approach to data and intellectual-property rights. By treating user-generated content as well as derived information and knowledge as a common-pool resource, we hypothesise that all participants can be compensated fairly for their input. To test this hypothesis, we undertake an extensive review of experimental, commercial and social participatory-sensing applications, from which we identify that a decentralised, community-oriented governance model is required to support this open approach. We show that the Institutional Analysis and Design framework as introduced by Elinor Ostrom, in conjunction with a framework for self-organising electronic institutions, can be used to give both an architectural and algorithmic base for the necessary governance model, in terms of operational and collective choice rules specified in computational logic. As a basis for understanding the effect of governance on these applications, we develop a testbed which joins our logical formulation of the knowledge commons with a generic model of the participatory-sensing problem. This requires a multi-agent platform for the simulation of autonomous and dynamic agents, and a method of executing the logical calculus in which our electronic institution is specified. To this end, firstly, we develop a general purpose, high performance platform for multi-agent based simulation, Presage2. Secondly, we propose a method for translating event-calculus axioms into rules compatible with business rule engines, and provide an implementation for JBoss Drools along with a suite of modules for electronic institutions. Through our simulations we show that, when building electronic institutions for managing participatory sensing as a knowledge commons, proper enfranchisement of agents (as outlined in Ostrom's work) is key to striking a balance between endurance, fairness and reduction of greedy behaviour. We conclude with a set of guidelines for engineering knowledge commons for the next generation of participatory-sensing applications.Open Acces