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Format: 1 Online-Ressource (8 p)

Content: This paper discusses the impacts of providing energy efficiency information to online shoppers. It lays out mechanisms by which this may improve appliance purchase and use decisions, raise awareness, improve innovation and align market forces with sustainability objectives. It finds that suitably structured light-touch requirements to provide mandatory information online can preserve and extend the benefits delivered by the Eco-labelling Directive. It also draws implications for broader use of information as a regulatory tool

Note: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 5, 2012 erstellt

Language: English

DOI: 10.2139/ssrn.2141967

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Format: 10 S.

Series Statement: The Rand paper series P-6900

Language: English

UID:

Format: 10 S.

Series Statement: The Rand paper series P-6900

Language: English

UID:

Format: 1 Online-Ressource (24 p)

Content: Much existing analysis of privacy seeks to clarify differences between data protection and privacy, e.g. by incorporating ethical rules like: voluntary participation; clear and optimised value; meaningful and informed consent; respect for privacy, identity and confidentiality preferences; ‘ethics by design’; and clarity regarding specific interests. These are all relational considerations, so attention naturally shifts from data protection to data governance and from individual privacy to relational privity. This is already bearing concrete fruit in e.g. data science and cyberphysical systems (including the IoT). But it is still relatively insensitive relationship structures; the objective of this research is ultimately to apply network game theory methods to the understanding of information access and utilisation structures, hoping to replace crude privacy, data protection and cybersecurity rules – which focus on the individual level (e.g. data protection as a fundamental right of individuals), pairwise links (confidentiality rules) and entire groups (security rules and ‘public information’) with something that more accurately reflects the importance and dynamics of structures as they have emerged in practice. Network game theory replaces: i) the ‘big group’ of non-cooperative games (where all the players ‘play together’) with explicit structures that determine who plays with whom; and ii) the ‘coalitions’ of cooperative game theory with a specific geometry of (binary or higher) interactions. To apply these tools to privacy and security, it is necessary to clarify the nodes and links that make up the network and the impact on what people know and do. It is already clear that informational or data privacy can be straightforwardly represented; people are the nodes, and access to or flows of their personal information determine the links. One contribution of the work described in this paper is to give privacy an explicit topological structure. Access and permitted actions define proximity and explicit contacts and contracts are supplemented by shared norms. People may be ‘close’ either in the sense that they are less private or secure from each other than from others or by having similar (consensus) views of privacy and security – and thus similar responses to unexpected developments, willingness to support changes in law and availability to enter new relationships. The present paper formalises privacy and security in terms of access as observation rules (how what is observable relates to private information) and protocols (who can observe whom and when). It considers how these affect the degree to which private information or private actions become common knowledge, and sketches a basic framework for introducing strategic considerations and for policy impact assessment. This allows: i) a characterisation of outcomes and the impact of rules and norms for different structures; ii) the analysis of models of the evolution of privacy, privity and security conventions along the lines of behavioural conventions (in particular that ‘slow-growth’ topologies favour rapid convergence to risk-dominant outcomes); and iii) modelling the evolution of networks along pairwise stability lines. Information shared (or withheld) changes the payoffs and alters higher-order beliefs embodying reputations or trust relations. While standard network game models have fixed strategies and payoffs (the evolution of conventions model) or fixed notions of what each player gets in each network structure (in the structural evolution model); the network privacy model allows these to change as information is shared and used. It also raises some questions such as whether the formation of common knowledge about private information or individual actions is an undesirable consequence of reasonable private protocols and whether mandated disclosure might remove some inequities and inefficiencies associated with partial privacy

Note: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments August 16, 2017 erstellt

Language: English

DOI: 10.2139/ssrn.2944251

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Format: 1 Online-Ressource (30 p)

Content: Recent work on privacy (e.g. WEIS 2013/4, Meaningful Consent in the Digital Economy project) recognises the unanticipated consequences of data-centred legal protections in a world of shifting relations between data and human actors. But the rules have not caught up with these changes, and the irreversible consequences of ‘make do and mend’ are not often taken into account when changing policy. Many of the most-protected ‘personal’ data are not personal at all, but are created to facilitate the operation of larger (e.g. administrative, economic, transport) systems or inadvertently generated by using such systems. The protection given to such data typically rests on notions of informed consent even in circumstances where such consent may be difficult to define, harder to give and nearly impossible to certify in meaningful ways. Such protections typically involve a mix of data collection, access and processing rules that are either imposed on behalf of individuals or are to be exercised by them. This approach adequately protects some personal interests, but not all – and is definitely not future-proof. Boundaries between allowing individuals to discover and pursue their interests on one side and behavioural manipulation on the other are often blurred. The costs (psychological and behavioural as well as economic and practical) of exercising control over one’s data are rarely taken into account as some instances of the Right to be Forgotten illustrate. The purposes for which privacy rights were constructed are often forgotten, or have not been reinterpreted in a world of ubiquitous monitoring data, multi-person ‘private exchanges,’ and multiple pathways through which data can be used to create and to capture value. Moreover, the parties who should be involved in making decisions – those connected by a network of informational relationships – are often not in contractual, practical or legal contact. These developments, associated with e.g. the Internet of Things, Cloud computing and big data analytics, should be recognised as challenging privacy rules and, more fundamentally, the adequacy of informed consent (e.g. to access specified data for specified purposes) as a means of managing innovative, flexible, and complex informational architectures. This paper presents a framework for organising these challenges using them to evaluate proposed policies, specifically in relation to complex, automated, automatic or autonomous data collection, processing and use. It argues for a movement away from a system of property rights based on individual consent to a values-based ‘privity’ regime – a collection of differentiated (relational as well as property) rights and consents that may be better able to accommodate innovations. Privity regimes (see deFillipis 2006) bundle together rights regarding e.g. confidential disclosure with ‘standing’ or voice options in relation to informational linkages. The impacts are examined through a game-theoretic comparison between the proposed privity regime and existing privacy rights in personal data markets that include: conventional ‘behavioural profiling’ and search; situations where third parties may have complementary roles conflicting interests in such data and where data have value in relation both to specific individuals and to larger groups (e.g. ‘real-world’ health data); n-sided markets on data platforms (including social and crowd-sourcing platforms with long and short memories); and the use of ‘privity-like’ rights inherited by data objects and by autonomous systems whose ownership may be shared among many people

Note: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 31, 2015 erstellt

Language: English

DOI: 10.2139/ssrn.2588027

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Format: 1 Online-Ressource (19 p)

Content: Economic interactions – especially online – generate data that stimulates strategic artificial intelligence (AI), machine learning (ML) and deep learning (DL) use: by businesses for predictive analytics, process optimisation and market power; by consumers for search, decision-making and (again) market power; and by governments for detecting criminal or harmful behaviour, gathering evidence and regulation. Not all uses increase competition and efficiency. One recent concern is algorithmic collusion (AC); whether revenue management algorithms can signal and implement tacitly collusive behaviour. This paper summarises theoretical and empirical evidence, considers how specific business machine learning methods may affect AC and whether consumer and regulator algorithms can detect or solve the resulting problems. It examines the links between Internet regulation and competition/consumer protection policy.Much early ML literature concentrated programmes ‘learning’ about their environments. A simple version would predict tomorrow’s prices from historical data to set profit-maximising prices. This could involve estimating prices or costs (assuming their behavioural rules), trying to identify behavioural rules or trying to influence rivals’ learning. Here, AI includes anything from fixed rules mapping data to prices to deep neural nets, ML is AI machines that program themselves to optimise specific objectives (thus having at least one ‘hidden layer’) and DL is ML with many hidden layers. Increased depth and thus computation makes behaviour an intricate convolution of data and programme history that is less visible those who programmed the system, let alone explainable to ‘outsiders.’ If many firms use ML, learning seeks a ‘moving target’ and may fail to converge or lead to unintended consequences.Conventional AC models use simple algorithms to demonstrate behaviour consistent with collusion in models of repeated interaction. It is not inevitable or classically collusive especially without good communications. More sophisticated approaches, however, suggest that populations of even simple AI agents can learn to adopt sophisticated reward/punishment strategies that sustain profitable outcomes. This paper considers further variations taking into account e.g. the influence of size and targeting of price deviations, finite-memory or dominance elimination strategies and the difference product characteristics (durability, quality uncertainty, purchase frequency) and search services can make. Simulation results illustrate a range of classic market inefficiencies (overshoot, convergence to prices between monopoly and oligopoly, cyclic behaviour and endogenous market-sharing collusion).From the regulatory perspective, it is not clear what is illegal and what could or should be banned. This raises questions of detecting AC (e.g. by DL) and limiting its spread or consequences. We consider: i) restrictions on information available to firms; ii) constraints on the speed or size of pricing changes; iii) Coding standards e.g. to incorporate regulatory compliance in ML objectives; and iv) algorithmic detection of specified anticompetitive behaviours. For iii), we show that populations using (e.g.) likelihood-ratio policy gradient reinforcement learning are more likely to converge to collusive behaviours (tit-for-tat) when they take other firms’ learning into account and more able to shape others’ learning depending on the prevalence of AI and the topology of information

Note: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 30, 2019 erstellt

Language: English

DOI: 10.2139/ssrn.3385319

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Format: 1 Online-Ressource (31 p)

Content: This paper, prepared as part of a larger study into long term data repositories (entitled RASSC - Repository Access Services Through the Supply Chain), analyses the business models and business cases appropriate to supply chain data repositories. It adapts the concept of a value chain to cover the initial, formal, eventual and informal relationships likely to develop on the RASSC platform, and to identify the way value arises and is captured. This, in turn, allows stakeholders to design and implement suitable business models that can enhance the value of existing supply relationships. By classifying business models and placing them in supply chain and broader economic contexts, it also supports eventual business case development and policy/regulatory analysis of what may be an emerging business area that complements supply chains across a range of sectors. Because the specific components are complex and rely for their accurate and persuasive implementation on specialised and context-dependent information, the deliverable seeks to provide a general framework illustrated with examples rather than a specific instance

Note: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 30, 2012 erstellt

Language: English

DOI: 10.2139/ssrn.2141913

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Format: 1 Online-Ressource (19 p)

Content: The expanding literature ...

Note: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 16, 2018 erstellt

Language: English

DOI: 10.2139/ssrn.3142109

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Format: 1 Online-Ressource (9 p)

Content: This paper presents a framework for analysing the reciprocal impact of technology trends and socioeconomic developments in the future Internet. It analyses the ‘Internet of X’ and its underlying technologies, and draws attention to policy-relevant network effects (structure and behaviour changes) and associated economics mechanisms, especially those relating to lock-in

Note: In: eChallenges e-2009 Conference Proceedings, Paul Cunningham and Miriam Cunningham (Eds), IIMC International Information Management Corporation Ltd., 2009 , Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 5, 2012 erstellt

Language: English

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Format: 1 Online-Ressource (14 p)

Content: The spread of malware (broadly defined) and other risks on electronic networks has frequently been studied using the tools of epidemiology. This approach has led to a number of important insights, but tends to be applied at the system design level and thus cannot account for a number of observable features and can produce misleading policy recommendations, especially in contexts where the risks, the structure of the network and individual (as well as collective) risk management behaviour change endogenously. The present paper extends the analysis by considering the interactions among: The spread of ‘infections’ – in other words the state of the population of networked nodes; The structure of the network – to whom (and how) the nodes are linked; and The strategies or behaviour adopted by individual nodes – including risk communication and risk management. The body of the paper develops some suggestive results from existing and ongoing research from related areas of theory and applies them to stylised aspects of the malware problem; the discussion also indicates areas for future work, especially in the latter part, which also develops some policy implications. In addition to the literature the paper draws on research being undertaken in connection with two specific projects: an investigation into privacy and security in cloud computing funded by the European Commission Directorate-General for Information Society and Media and an interdisciplinary study of the governance of livestock disease funded by the UK Biotechnology and Biological Sciences Research Council. It further benefits from the generous comments of colleagues at RAND Europe and the University of Warwick Departments of Economics, Biological Sciences, Systems Biology and e-Security. None of the material in this paper represents the position of any of the people or organisations whose support is acknowledged here and any errors remain my own

Note: In: TPRC 2011 , Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments August 15, 2010 erstellt

Language: English

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