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

… examine the impact on the trading funds’ business models of any changes to the current pricing, accessing and licensing regimes with the aim of:

• distinguishing more clearly what information is required by Government for public policy
• ensuring that this information is available as widely as possible in order to maximise the benefits to the wider UK economy, at a price that balances the provision of such access with the need for users to make a fair contribution to the cost of collecting the information in the long term.

The policy objectives of each of the trading funds will not form part of the assessment, but the review will consider the future of the trading fund model and how it impacts on the delivery of these objectives.

Presentation by David Clark (KCL): Developing Effective Psychological Treatments for Common Mental Health Problems

1. Anxiety disorders

   • ~ 1/2 of mental health problems
   • overly pessimistic view on outcomes etc
   • can become obsessional (+ fear that thoughts are self-realizing)
   • If beliefs are inconsistent why do they persist
   • panic attacks (~ 30% have them once/v. occasionally but realize that they are not dying). But in the disorder people might have had them 5000 times — how can they still think they are dying when it happens again?

2. Research Strategy:

   • identify core cog. abnormality
   • …

3. Example: social phobia

   • most common anxiety disorder (lifetime prevalence: 12%)
   • persistent: natural recovery rate is 37% over 12 years
   • marked underachievement
   • persists because:
   • shift to internal focus (which means ignore external)
   • use of internal information to infer how one appears to others (and as they are anxious this unreliable)
   • safety behaviour
   • test some of this
   • Do high socially anxious individuals have an internal attentional bias (Mansell, Clark, Ehlers 2003)
   • Evidence that socially anxious individuals have a distorted external perspective (Hackmann, Surawy and Clark 1999)
   • Evidence that onset of phobia correlated some stressful (bad) social event
   • Does negative self-image affect relation with others. Yes, to some extent (another Clark paper)
   • treatment (Cognitive Therapy)
   • attention training
   • drop safety behaviours (to test no adverse consequences)
   • video feedback
   • rescripting early memories
   • does CT pass the randomized controlled trial: YES
   • compare against no treatment
   • placebo
   • at least as effective as medication

4. Common disorders where CBT is effective as a sole treatment (recovery rate, controlled effect size):

   • Major depressive disorder: 50%, -
   • Panic disorder 75%, 2.8
   • PTSD: 80%, 2.3/1.2
   • Social phobia: 75% 2.6
   • Generalized anxiety disorder: 50% (77%)
   • OCD: 45%, 1.5
   • Also show that effects of CBT persist for anxiety (unlike psychotropic interventions where there is a high relapse rate)
   • depression slightly different as naturally recurrent — though CBT still effective (and complementary to medication). Hollon et al (2005) (Arch Gen Psychiat) compare medication vs CBT over long-term and shows CBT better.

5. Evidence that benefits of CBT extend outside of targeted syndrome. Beneficial effects for:

   • other mental health problems
   • work, family, social adjustment
   • employment (less sick days, moving to work)
   • but these effect sizes are lower bound (overall want SWB scores …)

6. Developing more effective (shorter) treatments

   • Traditional approach is 1h/w for 3-4 months
   • but 1-2h of ‘homework’ per day between visits
   • Now trying intensive 1w course (~ as effective at least for PTSD)
   • Also treatments with extra-focus (e.g. social phobia + work: found big impact on time to get back to work)
   • CBT with well-being emphasis. Fava et al (2005) (Psychotherapy and Psychomatics). Find CBT-WB > CBT but tiny sample, no blind assessment etc.

7. Major policy changes underway to increase access to CBT

Martin Knapp (LSE + KCL): Economics of Mental Health: Some Open Research Questions

1. Why mental health is different

   • breadth/multiplicity of need
   • association with crime + violence
   • associated with suicide
   • compulsion, stigma, complex links with ethnicity

2. Leading policy/practice themes

   1. stigma/rights/social exclusion
   2. funding
   3. Balance of Care
   4. Treatments
   5. Prevention

3. Social exclusion, stigma, etc

   • Participation-based approach
   • opportunities, socio-economics roles
   • Rights-based approach
   • stigma, discrimination, compulsory treatment
   • If i were suffering from mental health problems I don’t want anyone to know (Scotland): 50% in 2002 to 41% in 2006 (following a big campaign)
   • evidence in UK actually may be getting worse (16% 2000 to 22% in 2007 on similar question)
   • Equity: great variations (inequality greater for mental health than for income), esp by ethnicity.
   • Costs:
   • total cost of depression £9 billion (Thomas and Morris, Brit J Psychiatry 2003)
     • mostly productivity effects (not service or morbidity)
     • prob. underestimate as also have staff turnover, presenteeism
   • major impact of psychosis on life-time development [ed: not exactly surprising …]
   • homicide: Taylor and Gunn (Brit J Psychiatry) show that across various European countries between 5 and 20% or homicides committed by those who are mentally ill

4. Funding

   • Mental health spend as %tage of total public spend: England is highest in EU [ed: is this good or just that England has a lot of mental health issues]
   • Good efficiency arguments for intervening (cost-effective)
   • Schizophrenia: total cost ~ £54k per person per year (only a 1/3 hits the health system)

5. Balance of Care

   • Massive reduction in number psychiatric beds (personal preferences, social preferences etc)

6. Treatments

   • Does it work?
   • Is it cost-effective? etc
   • In 2000 only 53% of people with depression received treatment compatible with NICE guidelines
   • More attention to non-health interventions
   • particularly risk factors such as bullying, family violence, uncontrolled debt

7. Prevention

   • Inner London Longitudinal Study (ILLS)
   • Study of all 10y old in part of London in 1970
   • Categorise into groups from: “no problems at school” to “conduct disorder”
   • Estimate costs to society per child from 10 to 28 (education, criminal justice, social services etc)
     • no problems: ~ 7k, conduct: ~ 24k, conduct disorder: ~70k (mostly criminal justice)
   • 1970 British Cohort Study
   • earnings at age 30 by childhood need at age 10
   • no probs: ~24k, behavioural (lowest quartile): same, Cognitive (lowest quartile): 15k, emotional (not a great effect but interacts in a minor way with cognitive). Another study finds same effects for behavioural at age 32 but extended to 48 finds same -ve effect of cognitive issues.

In addition for ease of installation it has also been turned into a proper python package and registered on pypi so you can just do:

$ easy_install markdown2latex

Alternatively you can still get it straight from the repository at:

http://knowledgeforge.net/okftext/svn/trunk/python/markdown2latex/

http://rufuspollock.org/economics/papers/search_engines.pdf

Abstract

Internet search (or perhaps more accurately ‘web-search’) has grown exponentially over the last decade at an even more rapid rate than the Internet itself. Starting from nothing in the 1990s, today search is a multi-billion dollar business. Search engine providers such as Google and Yahoo! have become household names, and the use of a search engine, like use of the Web, is now a part of everyday life. The rapid growth of online search and its growing centrality to the ecology of the Internet raise a variety of questions for economists to answer. Why is the search engine market so concentrated and will it evolve towards monopoly? What are the implications of this concentration for different `participants’ (consumers, search engines, advertisers)? Does the fact that search engines act as ‘information gatekeepers’, determining, in effect, what can be found on the web, mean that search deserves particularly close attention from policy-makers? This paper supplies empirical and theoretical material with which to examine many of these questions. In particular, we (a) show that the already large levels of concentration are likely to continue (b) identify the consequences, negative and positive, of this outcome (c) discuss the possible regulatory interventions that policy-makers could utilize to address these.

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