miscellaneous factZ

Last week I attended FLOSS 2008, the second international workshop/network meeting on FLOSS (Free/Libre/Open Source software) in Rennes, France. I was presenting my paper Innovation and Imitation with and without Intellectual Property Rights (and would have offered discussant comments but the author of the paper I was scheduled to discuss had to pull out at the last minute). In addition to this I got to hear a variety of interesting talks. On some of these I was able to take notes which I have included below for the ‘delectation’ of anyone else who is interested.

Mikko Valimaki: IPR and Open Source Software

• Goodman and Myers (2005) — the 3G standard.
• Leveque and Meniere 2007: what does RAND mean
  • reasonable royalty is R = c (v1-v2)p where c is incremental costs of licensing, v1-v2 is gain from using this pattern over second-best.
• Other questions for royalty-setting
  • quality of volume of patents
  • early or late innovators
  • cumulative royalties or one-time fees
• But all models he knows of have non-zero royalty fees
  • [ed]: not surprising given that you will always get interior solutions
• Windows/Samba discussion
  • specific sets of terms
  • provide RF for the open source community
• Commission Decision para 783
  • “On balance, the possible negative impact of an order to supply on Microsoft’s incentives to innovate is outweighed by its positive impact on the level of innovation of the whole industry.”
• Nokia to acquire Symbian:
  • “a full platform will be available … under a royalty-free license … from the Foundation’s first day of operations … the Foundation will make selected components available as open source at launch.”
  • [ed]: Motivation here is clear: Nokia care about the hardware and for them software is a complementary good — which they therefore wish to be as cheap as possible. But this raises question as to what is being made open: is hardware patents or pure software patents (and if so how big a deal is this)

Stefan Koch: Efficiency of FLOSS Production

• Question of efficiency of open source development
• How much software did we get for our effort
  • Is OS a waste of resources?
• Discussion without much empirical basis
  • Claim: fast and cheap, high quality, finding bugs late is inefficient (actually large effort) — see IEEE Software 1999
• Completely unknown as no-one keeps time-sheets. So
  • Effort based on participation data
  • Effort based on product — look at software and ask how much effort would be needed in commercial environment
• Empirical research in open source
  • Mainly case studies
  • Helpful but need proper large-scale analysis
• Mined software repositories [ed: cf. today FLOSSMatrix, FLOSSMore]
  • 8,261 projects
  • 7,734,082 commits
  • 663M LOCs
  • resources and output is skewed: top decile of programmers: 79% of code base, second decile: 11%
• Effort estimation based on actual participation
  • active programmer months (define active as committing in a given month)
  • high correlation with LOC added in month
• Cumulate this number for each project
  • But not equal to a commercial person-month
  • How do we scale: use 18.4 h/w taken from stats for committers on Linux kernel
  • [ed:] this is the key assumption. The whole point is that FLOSS effort is not observed and they are using a measure of output (committing) and trying to infer actually activity
• Manpower function modelling:
  • Norden-Rayleigh model (1960)
  • Some set of problems N (unknown but finite)
  • Probs are solved independently and randomly (following Poisson)
  • This fits ok but has eventual decline in participation which does not occur
  • Modify this: in particular to allow introduction of new problems
    • Introduce in prop to original no. problems, in prop to current set of problems etc
    • Also have different learning rates
    • [ed: but isn’t the setup a little different. Really it is a question of success vs. non-success in terms of acquiring users + some kind of bound on amount of participation due either to fission or complexity]
• Product-based estimation
  • COCOMO 81 and COCOMO 2
• Results:
  • Comparison COCOMO - Norden-Rayleigh
  • For COCOMO 81 cannot find parameters favourable enough to explain Norden-Rayleigh curve
  • For COCOMO 2 can find parameters but very favourable
  • Suggest (roughly) that FLOSS very efficient (but not very rigorous)
• More formal estimation using all models etc
  • Norden-Rayleigh significantly below prodcut-based estimates (factor of 8 in mean)
• Interpretation
  • FLOSS v. efficient (self-selection for tasks etc)
  • Extremely high amount of non-programmer participation (1:7 relation …)
• [ed]: not sure about this generous view. Other explanations
  • No quality measurement (also mentioned by Koch)
    • OK: lot of code but low quality
  • (Related) Many sourceforge projects are incomplete, easy bit at the start
    • Later comes a lot of refactoring/writing documentation. This may display significant diminishing returns
  • Many FLOSS projects come from what were originally commercial projects. In that case:
    • code may have already been written
    • conceptual components have been done already
  • Trade-off of time vs. productivity
    • May be more productive to only work 10h a week but then product might not be ready for 10 years
• Form discussion
  • interesting point: Nokia thinking of moving to more FLOSS in-house because they can’t manage their 5-10k programmers centrally any more

Mickael Vicente: Shift to Competences Model: A Social Network Analysis of Open Source Professional Developers

• Robles 20007
  • Statistics on Debian showing increasing corporate involvement
• Social network extraction
  • Get repo logs
  • Create link between 2 developers if they have committed on the same file (non-directed graph)
    • Simplification: the best collaboration of each developer (directed graph) — pick other developer with whom they have committed most files in common
  • Longitudinal analysis
    • extract clusters
• Correlation with professional career
  • CV collected on Internet, personal web page etc (96% collected)
• Interesting data

Nicholas Radtke: What Makes FLOSS Projects Successful: An Agent-Based Model of FLOSS Projects

• Positive Characteristics of FLOSS
  • High quality (Low defect count: Chelf 2006)
  • Rapid development
  • Violates Brooks law (Rossi 2004)
  • Risky Business
• for every successful FLOSS project there are dozens of unsuccessful projects
• Corporate IT manager survey (2002)
  • 41% mention inability to hold someone responsible for software
• Attempts at Simulating FLOSS
  • SimCode (Dalle and David 2004)
  • OSsim (Waggstrom et al 2005)
  • …
  • K-Means stuff
• Simulate across landscape
  • Not social network
  • Focus on developer decision to join/contribute to projects (Agent-Based Modelling)
• Defining Success and Failure
  • Traditional metrics do not work well (on budget?)
  • Completion (Crowston et al. 2003)
  • Progression through maturity stages (Crowston and Scozzi 2002)
  • Number of developers
  • Mailing list activity
  • Project outdegree, Active developer count (Wang 2007)
• The Model Universe
  • Agents and projects
  • Agents:
    • Consumption: 0-1
    • Producer: 0-1
    • Resource: 0-1.5 (1=40h)
    • Memory: agents only aware of some subset of projects
    • Needs vector (preferences)
    • utility: linear sum of: similarity match + current popularity (current resources) + cumulative resources + download + f(maturity)
  • Projects:
    • resources needed
    • current resources
    • cumulative resources
    • download count
    • preferences: same as agent but converges towards those had by agents working on it
• Agents choose between projects each time period
  • have some randomness in that use multinomial logit: prob choose project i ~ exp(mu * Utility of project i)
• Results
  • Simulate over 250 time steps ~ 4 years
  • calibrate [ed: in a way I was not quite clear about]
  • compare simulation with empirical data from sourceforge
    • developers per project
    • projects per developer
  • Find that (from simulation data) downloads and cumulative resources are not important

Fabio Manenti: Dual Licensing in Open Source Software Markets

• Benefits of Going Open Source
  • feedback from community
  • network effects (usage)
  • competitive pressures (e.g. Netscape) [ed: not sure this is a benefit]
• Dual-licensing
  • Kosky (2007): 6% of representative sampl of European OSS business firms employ DL strategies

Alexia Gaudeul: Blogs and the Economics of Reciprocal (In-)Attention

• What blogs are
• Reasons for blogging
• Question: do you befriend (link) because of content produced or do you produce content because of friends
• General points
  • Market interactions only part of wider class of reciprocal relations
  • Time vs. money economics
  • Unique dataset, very detailed and complete, to test networked relations
• Model — but left out due to time
• Dataset: livejournal 2006
  • Sociology: teenagers to young adults (15 to 23), female (67\%), Americans (70\%)
  • Fast growth: created in 1999, 8M accounts, 1.3M active
  • FLOSS but for-profit (SaaS)
  • Great part from self-referential
  • Lively: 4 comments per post on average
  • Federated by communities: no. of communities per person 15
  • Journals updated for more than 2 years on avg
  • 70\% have posted in last 2 months
  • No. of entries: 1 every 2 days
  • No. of friends: 50 avg
  • Balance between friends and friends of
  • Balance between comments received / made
• Friendship patterns
  • May be balance but does not explain no. of friends of diff. individuals
  • Need to distinguish
    • Norm of reciprocity: more promiscuous bloggers accumulate friends
    • Content attractiveness
      1. Quality/freq. of posts
      2. Interactivity (comments per post)
• Regressions
  • Reciprocity: No. blogs read (friend) = b * number of readers (friend of) + error
  • Activity: No. readers = cX + error — X = matrix of ind. variables
  • Endogeneity issues [ed: all over the place)
  • Regress: ln(Friends) = ln(Friend of) + … (with instrumenting Friends Of on Activity so solve endogeneity issues)
    • Saturation around 400 friends seemingly (few with more)
  • Max no. of friendship when your no. friends = no. friends of (maybe)
    • A norm of reciprocity
  • Issues with endogeneity of activity (which was used to instrument friends of)

Sylvain Dejean

• Does ICT lead to the Internet lead to a global village or a cyber-balkan
• What leads to emergence of virtual commmunities
• Is the heterogeneity of contributions an impediment to self-organize
• How to manage virtual communities
• Agent-based model:
  • Individuals defined by some characteristics
  • Herfindahl index measures degree of self-organization [ed: why self-organization]
  • Communities change via selection and variation

After attending the IIOC conference last year I was back this weekend at the 2008 IIOC event which took place at Marymount University in Virginia. I presented the latest version of two of my papers: The Control of Porting in Two-Sided Markets and Forever Minus a Day? Theory and Empirics of Optimal Copyright Term.

I also provided discussant comments on Christopher Ellis’s and Wesley Wilson’s paper entitled Cartels, Price-Fixing, and Corporate Leniency Policy:What Doesn’t Kill Us Makes Us Stronger. In addition I include below some very partial notes on some of the sessions I attended — though activity in this regard was rather limited by the fact that, though there were more papers overall than last year (388 in total), sessions were organized into more breadth and less length.

Transaction Costs and Trolls: the Behaviour of Individual Inventors, Small Firms and Entrepreneurs in Patent Litigation (Gwendolyn Ball and Jay Kesan)

• Explore settlements in relation to patents. Questions:
  • How often do settlements happen relative to litigation
  • Are small firm and entrepreneurs at a major disadvantage in defending their patents
  • Or do patent trolls' use the threaof litigation toextort’ payments
    • NTP vs. RIM ($612M)
    • Saffron vs. Boston Scientific ($412M to individual doctor who had an infringed heart stent patent)
  • Does nature of defendant/plaintiff (L/M/S) affect likelihood of settlement
• Existing databases not so great
  • Only list trial outcomes not pre-trial outcomes
  • Often only list primary plaintiffs
  • Fix this and link patent litigation to companies
• Results
  • Claimed usually that 95% cases settle
    • In fact 8% are resolved at pre-trial (still expensive)
    • 4% settled at trial
    • so ~ 88% settle
  • Troll stuff:
    • 97 licensing firms as plaintiffs (none as defendants). These may be classic trolls but they are a small part of overall litigation.
    • Evidence shows that entrepreneurs and small inventors are very active (so do not seem particularly disadvantaged) and often sue each other rather than larger firms
    • Crudely: small inventors more likely to pursue a case to the end than large litigators
• Discussant comments:
  • Bessen and Meurer find $28M hit on firms facing litigation
  • Issues of correlated errors across cases
• My comments:
  • probably need to disaggregate across areas — after all no-one has suggested ‘trolling’ is an issue in traditional pharma
  • (for me) it would be useful to have an idea how many cases ’settle’ at the ‘letter stage’, that is, before anything even turns up in the court system. After all you only get to the courts (even with preliminaries) if you cannot sort out a license.

Prior Art - To Search or Not to Search (Vidya Atal)

• Alcacer + Gittelman 2006 showed 40% had prior art added by USPTO examiner
• 2/3 citations on an average patent added by USPTO
• Langinier + Marcoul (2003), Lampe (2007) — incentive to disclose prior art
• Issue of bad (non-novel) patents may be because people have poor incentives to search
• Mainly related this to fact that even a bad patent (if it gets past examination) has a +ve payoff

This January I met Alan Kirman at the Robinson Workshop on Rationality and Emotions. Over lunch we had a brief discussion about the difficulties of modern macroeconomics. I was therefore intrigued to see a new paper of his (co-authored with Peter Howitt, David Colander, Axel Leijonhufvud and Perry Mehrling) entitled Beyond DSGE Models: Towards an Empirically-Based Macroeconomics which was presented in January at the AEA conference (and looks like it will be appearing in the AER ‘Papers and Proceedings’).

The paper has much to say about the current state of macro, in particular the serious problems with DSGE (dynamic stochastic general equilibrium models) and where we should go from here. As the abstract puts it:

This paper argues that macro models should be as simple as possible, but not more so. Existing models are “more so” by far. It is time for the science of macro to step beyond representative agent, DSGE models and focus more on alternative heterogeneous agent macro models that take agent interaction, complexity, coordination problems and endogenous learning seriously. It further argues that as analytic work on these scientific models continues, policy-relevant models should be more empirically based; policy researchers should not approach the data with theoretical blinders on; instead, they should follow an engineering approach to policy analysis and let the data guide their choice of the relevant theory to apply.

It is worth quoting at some length from the paper in order to bring out the full ramifications of the story the authors tell:

Keynesianism Goes Wrong

With the development of macro econometric models in the 1950s, many of the Keynesian models were presented as having formal underpinnings of microeconomic theory and thus as providing a formal model of the macro economy. Specifically, IS/LM type models were too often presented as being “scientific” in this sense, rather than as the ad hoc engineering models that they were. Selective micro foundations were integrated into sectors of the models which give them the illusory appearance of being based on the axiomatic approach of General Equilibrium theory. This led to the economics of Keynes becoming separated from Keynesian economics.

The Reaction and a New Dawn (Rational Expectations and Neoclassical GE Models)

The exaggerated claims for the macro models of the 1960s led to a justifiable reaction by macroeconomists wanting to “do the science of macro right”, which meant bringing it up to the standards of rigor imposed by the General Equilibrium tradition. Thus, in the 1970s the formal modeling of macro in this spirit began, including work on the micro foundations of macroeconomics, construction of an explicit New Classical macroeconomic model, and the rational expectations approach. All of this work rightfully challenged the rigor of the previous work. The aim was to build a general equilibrium model of the macro economy based on explicit and fully formulated micro foundations.

But ‘Technical’ Difficulties Intervene

Given the difficulties inherent in such an approach, researchers started with a simple analytically tractable macro model which they hoped would be a stepping stone toward a more sensible macro model grounded in microfoundations. The problem is that the simple model was not susceptible to generalization, so the profession languished on the first step; and rational expectations representative agent models mysteriously became the only allowable modeling method. Moreover, such models were directly applied to policy even though they had little or no relevance. … [emphasis added]

But There Was a Reason For This: Other Stuff is Hard

The reason researchers clung to the rational expectations representative agent models for so long is not that they did not recognize their problems, but because of the analytical difficulties involved in moving beyond these models. Dropping the standard assumptions about agent rationality would complicate the already complicated models and abandoning the ad hoc representative agent assumption would leave them face to face with the difficulties raised by Sonnenschein, Mantel and Debreu. While the standard DSGE representative models may look daunting, it is the mathematical sophistication of the analysis and not the models themselves which are difficult. Conceptually, their technical difficulty pales in comparison to models with more realistic specifications: heterogeneous agents, statistical dynamics, multiple equilibria (or no equilibria), and endogenous learning. Yet, it is precisely such models that are needed if we are to start to capture the relevant intricacies of the macro economy.

Building more realistic models along these lines involves enormous work with little immediate payoff; one must either move beyond the extremely restrictive class of economic models to far more complicated analytic macro models, or one must replace the analytic modeling approach with virtual modeling. Happily, both changes are occurring; researchers are beginning to move on to models that attempt to deal with heterogeneous interacting agents, potential emergent macro properties, and behaviorally more varied and more realistic opportunistic agents. The papers in this session describe some of these new approaches. [emphasis added]

Some Closing Comments of My Own

So there you go: plenty of tough challenges and a big dose of humility. To some extent here it seems thing run on 30-40 years cycles: Keynesianism from 1945-1975, Rational Expectations DSGE from 1975-2005 and now we’re into the era of complexity and ‘loose’ tools with emphasis on empirics and heuristics rather than formal models. Whether this new approach will deliver more than the old is yet to be seen. After all, one reason that there are so many physicists getting interested in Economics and Finance is that the going is so hard in, e.g., condensed matter physics (superconductivity anyone …). If the economy really is so complex will we ever do any better at the macro scale than we do for the weather and if so will it not rely on some conceptual breakthrough rather than just doing using more hard-core dynamical systems theory and running more agent-based simulations?

That said, as the authors argue, the ’simple’ route isn’t working and the hardness of the path is no reason not to attempt it — an argument in many ways directly inverse to the traditional ‘drunkard-and-the-lamp’ approach in which we restrict our models, often beyond the point in which they remain relevant, in order to maintain analytical tractability. Thus, though cautious regarding what more ‘complexity-oriented’ methods can deliver, I am in wholehearted agreement with the authors that they justify much greater exploration.

Over the last couple of months for the purpose of my research on happiness/subjective-well-being I’ve been putting together some notes on theories of contextual judgement. The first part of these is now in a form suitable for public consumption and I’ve posted them at:

http://www.rufuspollock.org/economics/notes/theories-of-contextual-judgement/

I’ve just posted some early stage notes on models related to ‘Complex Systems’ with a particular eye towards those dealing with self-ordered criticality.

I was much much struck by generally pessimistic tone of Gregory Clark’s lengthy review in the JEL’s September issue of Avner Greif’s Institutions and the Path to the Modern Economy. These comments have wider implications for the application of economic tools (especially game theory) to the analysis of historical outcomes, particularly in relation to institutions, and I have therefore thought it worth excerpting from the review here (at some length).

When You Have More Variables than Data You Aren’t Explaining Anything

As noted, Greif defines an institution as a self-reinforcing set of behaviors. Greif pioneered in applying game theory to historical institutional analysis and his 1993 study of the Maghribi traders remains a classic of this still modest genre. This was certainly an exciting development for economists. For the first time, [sic] seemingly grounded the explanation of informal institutions in optimizing individual rational behavior. Behaviors that would seem to the layman to be based on blind irrational custom could be shown to be consistent with individual optimization. Given the incredible intellectual elaboration of game theory, and its meager harvest in terms of actual economic applications, the finding was welcome to both game theorists and to economic historians. [ed: a tough but fair assessment …] The Maghribi study also allowed for the possibilities of institutional change resulting just from changes in parameters. Since the equilibrium depended on certain parameter values, changes in transportation costs or observability could terminate the old equilibrium and lead to a new one. The 1993 article seemed to point to new micro foundations for institutions that would ground them in individual maximizing behavior.

But this book is almost certainly not what many economists who welcomed the 1993 article expected as the generalization of its ideas. Some indeed will be shocked by, and perhaps hostile to, the path Greif has taken. Were economists of a more literary bent, the word apostasy would be on their lips. In a search for generality, Greif concludes that such a set of limited rational actor assumptions is not constraining enough to describe real-world institutions. For a start, “multiple equilibria usually exist in the repeated situations central to institutional analysis” (p. 125). There have to be more constraints on the structure of the interaction to explain the equilibrium. These constraints include “cognitive norms” (p. 128) as well as “the social and normative foundation of behavior” (p. 143). Issues such as “losses of esteem,” “norms,” “fairness,” or “social exchange” have to be introduced. Also such social and normative behavior is “situationally contingent” (p. 144). [ed: and we now have so many parameters we could probably explain anything …]

Greif posits this as just an extension and elaboration of the original individualistic rational-actor game theoretic ideas. Once we are compelled to admit, however, into the explanatory apparatus almost the entire sociological zoo of ill defined and unmeasurable constructs, we lose all explanatory power. Explanatory power requires few objects and small degrees of freedom. Greif notes that “a useful feature of game theory is that it allows us to study all intertransactional linkages—economic, coercive, social and normative—simultaneously” (p. 147). But he does not seem to appreciate the price of this generality in terms of testability. All we are left with is the idea that people operating within institutions act as they do because, given the cognitive, intellectual, cultural, and normative constraints they face, their actions seem to them as being the best available. But, in an informal sense, we knew that already. Without any consideration of the ins and outs of game theory, we can appreciate that any lasting institution likely constitutes some set of self-reinforcing behaviors. Yanomamo males, for example, engaged in recurrent raids against other bands aimed at capturing women and revenging previous raids (Napoleon A. Chagnon 1983). This was clearly an institution in the sense of Greif and must be maintained by some kind of self-reinforcing set of behaviors. But we knew that, even if we had never studied game theory. So what insights have we gained from page after page of elaboration on the idea of equilibria and the elements that enter into them (pp. 124-53)? If we were able to reduce all such social equilibria to a game theory equilibrium of purely self interested rational individuals interacting with common knowledge that would be a radical, novel, and testable theory. This book denies that possibility, but without providing any alternative that has empirical content. [pp. 735-736, emphasis added]

The Problem of Too Many Equilibria (in Dynamic Games with Beliefs)

… Greif here starts from the basis that we will never be able to predict institutional structure from exogenous features of the situation—including institutional history. … Given the many potential stable equilibria in each institutional context, the outcomes are inherently unknowable. After the attention given to elaborating the theory of institutional stability and dynamics in the preceding 350 pages, this conclusion comes as something of a surprise. The structure and tone of the previous discussion is that of laying the groundwork for a theory of institutions. The reader now learns that the extended theory encompasses a perhaps uncountable number of possible institutional equilibria, so that there can be no advance prediction.

Just as deductive methods cannot succeed, Greif asserts also that inductive generalization about institutional forms will also fail to reveal any patterns. This is because unobservable elements of the situation—beliefs and norms—are crucial to the determination of the outcome. The same observable elements will be associated with radically different institutional equilibria. … [p.737]

Case Studies (and Historical Anecdote) Aren’t Economics (or Economic History

[The empirical approach recommended by Greif] As conducted in the book, [this] is essentially the method of “analytical narratives” popularized by Greif and Robert Bates, Margaret Levi, Jean-Laurent Rosenthal, and Weingast. An analytical narrative consists of matching institutional detail to a formal, or more often informal, interpretation of the situation as some kind of rational choice equilibrium, interpreted in the broad sense above (Bates et al. 1998). It is not clear how this is distinguished from such things as Harvard Business School case studies. As applied by Greif and his colleagues, an “analytical narrative” seems to be just an interpretation of an institution in terms of a loosely defined equilibrium. This is fine as an approach to generating hypotheses, but as an endpoint of analysis, as it generally is in the book, it offers little conviction. [p. 737]

In Conclusion: There Isn’t Much of a Future

… Greif intends in his book to develop at least the outline of a new, micro grounded theory of institutions. Stating, explaining, and elaborating this theory takes 503 densely written pages, including a primer on game theory. By the end, however, this reviewer, to the contrary, read it mostly as a demonstration of the impossibility of a systematic account of institutions along the lines he proposes. The efflorescence of concepts, combined with the constriction of possible empirical tests, makes … prediction and testing impossible. And this shows in the case studies conducted in the book. Each institution in his formulation has to be analyzed in its full idiosyncracy, aided by the expert judgment of the investigator as to the social and epistemological context. But, as we saw in the case of the Podesteria, that kind of analysis, even in the hands of a careful enquirer like Greif, is fraught with the danger of conflating conjecture and fact. Kant’s Prolegomena to any Future Metaphysics as a Science never led to his proposed science of metaphysics. Unfortunately Greif’s Prolegomena to a future institutional theory similarly serves mainly to indicate the barriers to a science of institutions.

Conference at UCL entitled, Trust and Triviality: Where is the Internet Going

Date: 2004-11-12

General

Medium doesn’t matter (i trust the other person i am speaking to I don’t worry about the telephone). RP: what about e-voting.

Trade-off between safety and access to information. First it must be acknowledge that there is a trade-off. This trade-off can be improved but will always remain. Example of schools.

Quality, truth, trust and elitism.

• Regulation central stuff from the ex-ITV guy. Duty of truth??
• Top-down allows control and regulation.
• Bottom up allows for free speech - but he claims a very dubious free speech.
• does internet narrow or widen points of view?
• Craig’s list.
• Comment on wikipedia. Democratization of production of information but is it reliable.
• RP: What scares me is this assumption (made by e.g. ITV guy) that the internet is uncontrollable. Unfortunately it is all too censorable.

Ed Richards, Senior Partner OfCom, Strategy and Market Developments

• In next debate over communications act will need to have public debate.
• (!!) Need effective DRM to create trust in the online world
• (!!) Bad: 4.7 million UK users have knowingly downloaded illegal content
• Mentioned EUCD etc. No particular emphasis but simply as a fact of life
• User don’t want to be criminalized they want to use iTunes ….
• Young generation need to be focused on to address this idea that music should be free. This culture of freeness is pervasive among the younger generation.
• KellersInformationSphere.com
• Twin track: creative commons along side a DRM commercial IPR environment. Creative Commons embraced.
• What could undermine this: spam, child protection issues,

Questions

• RP: wonderful to hear this support and interest in a creative commons [ed: as concept and as group]. One thing I am very interested in is a direct participation by the Government in nurturing this Commons - a role they have obviously long taken in the traditional academic and artistic spheres.

  • In response: we might want to ask that if content is generated by the govt why should it NOT be released in a creative commons manner. Now I can see there might be issues with this approach, for example if content is sourced from independent producers, but still I think you start from a position of why not.
  • raised issue of sustainability
  • RP: didn’t really know about non-commercial licenses

• Guy from Internet Watchdog: We don’t want to close down the internet or regulate it formally but we want the US to see the internet is not the Wild West and that something nees to be done

• Red-headed women: Lessig + issues with very strong IP protection. Aren’t there major problems with over-strong IP.

• ‘For my money the DMCA goes far too far’ … ‘It makes neither economic and cultural sense’ … ‘
• RP: they all know about Lessig
• DRM and stopping consumers thinking they should get stuff for free

Earl of Selbourne: Cyber Trust and Information Security

RP: Data Collection in Government: * Value of information and collection costs. * Data acquisition costs often not evaluated. * If information not reliable what value is it.

Own Thoughts:

• Brands
  • As what? Are they are certification or more than that.
  • Clearly more than that. Often not about information at all but about creating an image
• For me there is no silver bullet to solve the trust and reliability problem. Particularly as this can be in direct tension with a desire to have a very open/free approach to dissemination.
• The recursion argument. Suppose I receive piece of information X. How do I know that this piece of information is reliable/true/correct. Have following options:
  1. I have my own knowledge that allows me to check validity.
  2. Find an expert who I already trust who can tell me validity or not. But how did i find and verify the expert. At this point have to recurse. Verification knowledge may be of two kinds:
  3. Direct verification. I actually know the area and can judge from my own direct knowledge.
  4. I know something about the characeteristic of the information provider (e.g. they provide footnotes, they have a prestigious reputation for honesty, they have a right of reply that is administered honestly). This is indirect verification.

Relation to information bandwidth. Can often work out that an article is for or against a particular information with minimal processing. Levels of processing. Can take principal components approach (e.g. presence of footnotes) as a way of reducing the amount of information to process.

Can consider reliability of information in a social/truth sense as similar to information reliability issues in traditional information theory. I receive a signal. problem is the simple statistical mechanisms for analysing corruption of signals over nosiy channels are not appropriate to that relating to social reliability (are there facts true or not)

Are chinese whispers statistically like degradation of signals in traditional IT areas?