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From bikeshare to data coop

From bikeshare to data coop

Over the last couple of months I’ve been getting to know the team at Monash BikeShare. I’ve followed their intrepid lead, huffing and puffing my way around the Monash University campus. They’ve shared their stories of rebuff and conquest from their epic start-up trek. And their youthful enthusiasm leaves them with plenty of runway ahead to pursue some grand plans. All-in-all they’ve got a great springboard from which to pursue their bikeshare dreams…

Australia’s most successful bikeshare

So how did the team take an a unloved scheme and turn it into Australia’s most successful bike share? Like all well executed plans, it looks simple in hindsight.

Their first step was to implement the Donkey Kong model of user engagement – they identified the biggest barriers to user take-up and sought to remove them:

  • Cost – the scheme was introduced with a membership fee structure that had proved an immediate disincentive to potential users. The team convinced Monash University that it would be better to have people using the bikes for free than to have those same bikes rusting in a shed.
  • Awareness – Notwithstanding the relative visibility of the bikes around campus, there remained the problem of people’s awareness of how and when to use them. To solve this, the team sought to draw in separate groups within the campus to show how the bikes could directly benefit them. By ratcheting up their use by specific users, the team were able to increase the visibility of the bikes actually being used – and thereby tripping the tipping point for network effects.

Know your customer

There’s no doubt getting a critical mass of people to use the bikes was a great achievement for the team. Perhaps the more intriguing thing is their understanding of the way the business works. On the face of it, you’d think that the customers of the bikeshare are the folk who ride the bikes. Not so, say the team, they see their primary customer as Monash University. This starts to make sense when you break down the benefits.

When it is easier for people to get around the campus, Monash University can:

  • Help IT and support staff get to where they are needed;
  • Understand how people are moving across the campus;
  • Infer which facilities and areas are being used – both in and out of term; and,
  • Encourage better and more timely attendance by students.

We can start to see that the potential benefits of sharing schemes in a digitally connected world are not simply the immediate and most obvious ones around the consumer getting to use the bike. For a relatively small investment, Monash can start to generate some pretty interesting returns…

Bikeshare as a data coop

Which leads us to potentially the most interesting part of the conversation. If the customer of the bikeshare is Monash University, as it pays to derive the aforementioned benefits, what are users?

You could argue that they are the beneficiaries of a fabulous free service.

But we think there’s more to it than that. This is the same situation that has been played out in the models employed by Facebook, Twitter and anyone else that seeks to monetise the consumer. We get to use their platform, and they get to sell our attention and data. The costs of getting the platform up are minor compared to the payoff of locking in the network.

That’s why we believe that there is a different model. That this is the perfect opportunity for a new type of consumer coop – where the members benefit from using the bikes, and where the customers are those organisations that derive second order benefits. The members own the bikes, the data and the platform. The coop negotiates the terms with the customers.

Controlling our data

See this is about more than sharing bikes. As the mesh – as Lisa Gansky calls it – sends its tentacles ever deeper, the real world gets mapped in greater and greater virtual detail. So that while the sharing of assets and infrastructure get more efficient, so too does the information about those that are using them. Our customers want to know more about us.

You want to hop in that self-driving car? Sure just wave your chip, and we’ll map you into the grid. Think about the types of data that the self-driving car company will want on you. They’ll want to check that you wear deodorant and haven’t trashed any self-driving cars lately. Our reputation will be built by the things we do and the data exhaust that comes with it.

Regular readers will know that I believe data coops offer a way through to the next place. That our personal data is best managed in ways that enables the individual to maximise control while still allowing the collective to optimise value in aggregate. Having had a good look at the Monash BikeShare, it looks like a great place for the personal data revolution to start….

 

When algorithms rule the world

 

WorkSmart – organising for under 30’s

WorkSmart – organising for under 30’s

Just signed up to WorkSmart – the UK Trade Union Council’s freshly hatched plan to help organise 21-30 year old’s. Now okay, I’m a little outside the target demographic (perhaps a lot), but I’m interested in understanding how they’re positioning it.

What is WorkSmart?

WorkSmart is a set of tools and content to help younger workers take control of their career.

Currently, there isn’t much to see. I was sent a link to a short survey that tested my perspectives on work – how fulfilling it is, do I feel like I am in control, and my level of motivation. It promises to tailor the experience for me when the app is released. I signed up to know more about ‘how to progress my work’ and ‘how to build better relationships’ – when those bits are released.

Designed for two-thumbed typists

The TUC have invested many hours in seeking to design an experience that will appeal to the younger demographic. Emails are liberally smattered with emoticons. Text is very brief in the best post-modernist tradition. And the interactions are quick.

The point is to get younger workers engaging with an offer that helps them build their confidence, motivation and understanding – and helps break down the barriers to organising collectively. Over the course of younger workers’ engagement with the offer, we will introduce rights info, and get younger workers thinking about problems at work and how to work with their colleagues to overcome them.

Mint 🙂

Rebirthing trade unions

They’ve also downplayed the role of trade unions. As Antonia Bance (@antoniabance) notes ‘these younger workers thought unions were for other people – older people, public sector workers, people fixed in their career. And you could hear the impact of atomisation in their feedback to us – young workers didn’t feel able to trust their colleagues.” The aim is to introduce a paid offer – WorkSmart Extra – that incorporates union membership once they understand its value.

Finally, and the bit that particularly resonates with me:

the plan then is to start to spot emerging leaders, common issues, and clusters of members with the same employer.

Our experience has demonstrated that organising remains a face-to-face activity. Where technology can help is accelerating distriibuted organising. Ensuring that those folk that are willing to gather others and lead their local initiatives are well supported. It’s good to see that this is front and centre of the TUC’s thinking…

 


Good Work in the Machine Age

The need for this kind of thinking was starkly demonstrated in some research that the RSA has just released:

Question: How prepared are the following institutions to protect workers from the effect of new technologies?

Answer: Well prepared – trade unions 18%, tech companies 37%, employers 36%

 


 

SMart-eu – Individual agency with the power of the tribe

SMart-eu – Individual agency with the power of the tribe

It was delightful to have the opportunity to attend a couple of events last week where Lieza Dessein (current Board member and Project & Community Manager) introduced the SMart cooperative to various audiences. The reception was very promising with some of Australia’s more progressive thinking trade unions like the AMWU and NUW leading the way in considering how this model could be introduced into Australia. Following are my notes…

How SMart works

The aim of SMart is to assist skilled freelancers in managing their business administration. It does this by helping with contracting, invoicing and associated functions like insurance, accounting and tax. These functions are primarily managed through the SMart IT platform.

Additionally, every member is allocated a personal advisor to provide career and legal support. It’s intentionally a high touch approach. As there are ~50 advisers for ~20,000 active members, every advisor has 400 potential clients to work with. As a former adviser, Lieza confirmed that this is a number that works in practice.

The interesting thing about SMart is that it brings the power of a cooperative to freelancers – blending independence and flexibility with the strengths of a collective organisation. So for example, as a cooperative it can:

  • Spread the risks relating to payment and debt collection across its members through a mutual guarantee fund. This means that members can be paid for their work within 7 days of a contract being completed and SMart will then manage the debt collection process; and,
  • Aggregate purchasing power to enable better access to services such as creative hubs, equipment hire etc, and to business support such as insurance and training.

SMart has helped 90,000 people in Belguim since its inception in 1998 – artists, architects, journalists, IT developers, graphic designers, dog-walkers etc. It is expanding into 9 European countries currently, and Canada has a project underway to explore how the model could be applied within its borders.

SMart’s business model – a worker cooperative

The core idea behind SMart is that members create a pooled capability that can be shared without fear or favour – it’s a new type of worker cooperative. The resources and services of the cooperative are financed by collecting revenue based on the volume of business undertaken on the platform. As freelancers typically have irregular income, each member is participating according to their billing capabilities. It’s a system based on solidarity. This pooling system enables 100% of the members to benefit from SMart’s services when, based on billing capacities only, SMart services are covered by 20% of its active members.

To get a sense of SMart’s operating model, following are its financials from 2015 and 2016. (Note that they are translated from French and there is little by the way of notes to the accounts, so any analysis is necessarily superficial.)

First up, a snapshot of SMart’s revenue. When a freelancer becomes a member, they can use the platform to contract with their clients. In doing so, they actually become an employee of SMart during the term of a contract – which means that SMart reports the gross revenue of all their members.

Revenue 2015 (€) 2016 (€)
“Activities” turnover 72,071,039.34 58.64% 81,047,629.14 59.56%
“Contracts” turnover 50,826,191.89 41.36% 55,033,418.10 40.44%
Total Revenue 122,897,231.23 136,081,047.24

 

For the same reason, SMart’s expenses include the wages, commissions and other fees that are paid to members as their employer.

Expenses 2015 (€) 2016 (€)
Copyright concessions 2,784,950.84 2.27% 3,389,905.72 2.49%
Fees, Purchases & Charges 17,473,964.89 14.22% 18,379,542.19 13.51%
Gross wages 60,476,652.15 49.21% 66,977,960.82 49.22%
Employer costs 33,684,512.96 27.41% 35,479,492.35 26.07%
Participation of members in shared costs (6.5% of sales) 8,075,812.18 6.57% 8,908,889.95 6.55%
Budget not consumed 401,338.21 0.33% 2,945,256.21 2.16%
Total Expenses 122,897,231.23 136,081,047.24

 

To get a clearer picture of the operating performance of SMart, they also provide a ‘clean’ income statement where the activities of members are excluded from the analysis. I have included the complete income statement at the end of this article.

Revenue

If we assume that capitalized platform development is largely offset against depreciation and amortisation, then there are really only two drivers of income:

  • Participation of members in shared costs – Members share in the costs of running SMart by effectively paying a fee of 6.5% of the value of the contracts that they enter into on the platform. You can see this number flow through from the gross revenue number reported (ie. 6.5% of €136m is ~€8.9m).
  • Benefits from pooling – This revenue item contributes as much again as the 6.5% fee – and it is not clear exactly what it relates to from the accounts. The reference to ‘rebates and reduced commercial charges’ implies that SMart is receiving a margin on products and services that it provides to its members. In essence, the volume of administrative, tax, commercial transactions gives rise to scale benefits that the SMart cooperative collects on behalf of its members.
Expenses

Given that there are approximately 20,000 currently active members, the average annual cost of the entire platform is €850 per member. If the principle that 20% of the members effectively bear 100% of the costs holds true, then the average cost per year for these folk would be €4250. Either way, these costs seem pretty reasonable given the services provided!

  • Permanent staff – SMart has intentionally pursued a high-touch service model. It believes that by marrying the technology platform with expert advice, its members are best able to learn and develop their businesses. For this reason the largest component of its permanent staff are personal advisors and legal specialists. Additionally it has a large IT team to support this infrastructure that must be tailored to each legal jurisdiction. It also requires a core team for debt collection and contract management.
  • Bankruptcy losses – Based on these two years, SMart has a reasonably high bad debts experience. As Lieza explained, the losses from bankruptcy arise principally from bankruptcies by major employers (SMart will pay out the employees even if the employer is unable to). While the bad debts experience may be material, the social benefit of providing this safety net to members is significant.

Opportunities for the model in Australia

This model, whether in whole in or in part, has direct application in the Australian context. Following are some reflections on the opportunity and how it could work.

Growth in the gig economy

With at least 8% of the workforce now considered to be working in the gig economy, which may well understate the true extent of the casualization of work, the need to provide services that can effectively help these workers is already substantial and growing. This could include the SMart suite acclimated to Australia:

  • Career advice, legal support & training
  • Standardised contracts and cashflow management
  • Insurance & superannuation
  • Regulatory and tax management
  • Shared resources such as co-working spaces

Sharing technology

The platform used by SMart is highly customised to the European context. Even if the tech stack can be installed on an Australian domiciled server, it will require customization and integrations with the likes of the ATO, WorkCover, ASIC as well as an API for industry super funds and local accounting packages to draw upon. There is also the question of ongoing development and maintenance of the ‘core’ and the customised components. How will a federated SMart infrastructure be governed and developed across an international user base? This raises questions whether importing the SMart model is really about protocols and processes – and what technology could be efficiently shared.

Industry agnostic versus industry specific

The approach taken by SMart is very much industry agnostic. They can be used by any skilled freelancer regardless of the type of work they do. In their experience, freelancers are often career polymaths moving from industry to industry, even over the course of a year. To effectively spread the risks and costs of the business, they need to adopt this approach.

In Australia, there may be advantages to breaking at least parts of the model into industry segments. For example, it may assist to have specialization around member communication, career advice, micro-financing, training and standardization of contracts. This specialization could occur within a single entity or through the creation of industry specific organisations.

The challenge then is how to aggregate those functions that are common. For example, the mutual guarantee fund and the ‘core’ technology components could be best shared across industries.

Online marketplaces

SMart has intentionally eschewed setting up marketplaces for its members. Their view is that this is a business that could conflict with their single-minded representation of skilled freelancers. As online markets are generally industry or skill specific, this may support the notion that implementations of the model are industry specific. For example, the creation of a ‘pacemaker coop’ in the graphic design sector could have material benefits in bringing up standards for all workers (following the Stocksy model).

Freelancers as employees

In the Australian landscape, freelancers are typically required to register for their own ABN. They can have all the same problems in managing their back-office, payments, training, and super – but they do so from the perspective of a registered sole proprietor. This may create barriers, whether cultural, legal or mechanical, for the ‘freelancer as employee’ model.

Data coop

SMart have taken a very constrained approach to data, for example, avoiding any temptation to undertake deep analytics of their member base. This approach meant that navigating the recent introduction of GPDR was relatively straight-forward for them. My view is that coops offer a very attractive way for enabling individuals to better manage their data for both their own benefit and collectively. This is exactly where the work with the farming sector has taken us. Given this, there is a substantial opportunity for the SMart model to lead to better data management tools and capabilities for its members.

Where to start with an Australian model?

Any member-owned organisation must grow from the ground up. One of the challenges with introducing the SMart cooperative model is how to gather freelancers when, almost by definition, they are a disaggregated lot. In a sense, this supports that notion that the starting point is in single industries where the gig economy is dominant (eg. graphic designers). This would allow industry representative bodies to focus on the benefits to the workers in that sector (for example, the AMWU represents graphic designers). Ideally, these organisations have the existing organising capabilities and resources to bring freelancers, government, and industry stakeholders to the table to effectively execute a coherent strategy.


 

SMart’s ‘clean’ income statement

2015 (€) 2016 (€)
Revenue
Membership fees 347,243.75 1.76% 439,600.00 2.09%
Participation of members in shared costs (6.5% of sales) 8,075,812.18 40.90% 8,908,889.95 42.35%
Member services

(Space & equipment rentals, vans)

677,731.86 3.43% 613,079.55 2.91%
External customer services 457,158.26 2.32% 468,846.60 2.23%
Capitalized production

(Intangible investments)

1,807,195.77 9.15% 1,268,793.98 6.03%
Benefits from pooling

(Reduced charges, rebates)

8,008,475.27 40.56% 8,986,268.77 42.72%
Subsidies

(APE, Activa, Continuing Education)

113,070.89 0.57% 108,476.35 0.52%
Others revenue 259,459.22 1.31% 242,839.79 1.15%
Total Revenue 19,746,147.20 21,036,794.99
Expenses
Other expenses 107,892.56 0.66% 237,545.35 1.40%
External charges

(Rents, services, purchases)

4,913,320.17 30.12% 4,783,497.21 28.14%
Financial expenses 460,397.68 2.82% 134,407.05 0.79%
Depreciation allowance 1,724,918.98 10.58% 2,045,860.45 12.04%
Permanent staff

(148 FTEs in 2016)

8,594,555.25 52.69% 8,886,762.39 52.28%
Bankruptcy losses 508,927.92 3.12% 909,241.47 5.35%
Total Expenses 16,310,012.56 16,997,313.92
Surplus 3,436,134.64 4,039,481.07
Trending coops & mutuals in Australia

Trending coops & mutuals in Australia

Hard data on co-operatives and mutuals in Australia is a little scarce. Perhaps it’s because they cover so much territory – from the very large to the very local. So some quick cuts from the CME database.

Not surprisingly the location breakdown of CME’s pretty much echos population density…

And the type is interesting insofar that if we mapped the same by assets or revenue rather than sheer number of entities, then it would be the exact opposite – superannuation funds would be the largest by a fair margin, then health funds… and coops.

 

What perhaps is more interesting is seeing how the breakdown by industry maps to emerging trends in the sector:

Emerging trends – consumer coops

  • Community owned assets – The Housing and the Sports & Recreation sector are the most numerous by far – with groups as diverse as model railway operators to the Hells Angels using coops. They often use coops as a mechanism to gather management and ownership of shared infrastructure.  We’re seeing emerging interest in the coop housing sector for this purpose from specific demographics such as aged care or respite.
  • Shared financial services – still going strong when it comes to aggregating consumers money with financial services (aka credit unions, building societies, mutual banks etc), health insurance, superannuation funds. Merger activity and rebranding is often leading these entities to become increasingly remote from the constituencies that created them.
  • Education, Training & Childcare – numbers are swelled by public schools using favourable legislative treatment to create coops for building projects. Having said that, the childcare sector is another where workers and community can come together the use the coop structure to deliver better outcomes.
  • Information & Media – the community radio sector in Australia is huge with some 22,000 volunteers contributing over $300m in value to the community. Notably, the local and community nature of these operations has provided natural protection against the disruption that has been rippling through this sector.
  • Retailing – community ownership of retailers is a growing category of coops. So while most retailing coops are community owned stores there is also an emerging recognition that promoting consumer-ownership is the best defence against online disruption. If we follow in the footsteps of the UK, then we can expect more retailers to be acquired via coops – such as pubs, service stations, your local bakery.
  • Utilities – current energy retailers are pricing their offers to promote the emergence of community owned energy providers. We’re seeing wide interest across Australia in renewable energy projects that are likely to leverage the coop model.

Emerging trends – producer coops

  • Agribusiness and Fishing – Some of Australia’s more prominent agricultural coops were demutualised recently (Murray Goulburn and Namoi Cotton). Whether their members were better served by the coop structure is a moot point. Conversely, the federally funded Farming Together Program has sparked another crop of coops into existence. They are yet to appear in the data. Perhaps the biggest emerging trend is this sector is the wave of new technology that is being propelled at farmers. The potential impact on farmers has encouraged many to consider the role of coops in helping them manage their data both individually and in aggregate.
  • Arts & Culture – with 8% of the Australian workforce now part of the gig economy, we are expecting to see the arrival of platform coops that can help these workers to aggregate their needs and better protect their interests. As the Arts & Culture sector has a long history in using coops to manage resources, they could be well placed to emulate the SMartEU and Stocksy models in Australia.
  • Wholesalers, Professional Services, Purchasing Services & Shared Services – the industry aggregator coop remains alive and well despite the best efforts of Amazon.

 


 

A data sharing idea for cooperatives

A data sharing idea for cooperatives

Some days the new world order just plumps itself down in front of you like an overweight chook…  So today while scraping Tim Mazzarol’s fabulous cooperative and mutual database (here), I got to thinking about what was going on here.

The website gives access to the most complete list of co-operatives and mutuals in the country. It is all publicly available. And yet, the developers didn’t make it easy to download the database.

Why create the artificial barrier? Perhaps there is some residual sense of ownership of the data. Perhaps it’s an attempt to make the site a destination for accessing the data. It’s a little hard to know.

The thing is that this approach is all back-to-front. It’s the kind of thinking that gave us CRM’s that pretend that customer data is something you own. It is not the way the emerging data-driven culture works.

Data co-operatives are the solution

We are moving to a world where we choose to share data. It becomes an asset that we control – we need to be motivated to share it (even if it means agreeing to your capricious terms of service Mr Zuckerberg).

There is an opportunity in this. At incubator.coop, we have been working on developing a co-operative operating system. A software-as-a-service model that will enable coops of whatever size to access an integrated website, member engagement app, and share registry. It’s intended to be a co-operatively owned solution for the co-operative sector.

Now here’s the thing. Very much like painting the Sydney Harbour Bridge, the CEMI database is out-of-date by the time it is finished. It relies on the collection of static data. At its simplest however the coop mgt system offers a way for coops to share their data. They can choose what data they share to create a dynamically updating database that is truly open for anyone to use. And open, accessible data – that is shared by those that control it – is the way value can be created in the new world order.

 


 

Of loose cannons on the world’s deck

Of loose cannons on the world’s deck

Back in 1998 as a part of a Masters in International Politics, I wrote an essay on monetary unions and the future of the Euro. At the time convergence trades were all the rage and the soon-to-be members were busy pretending they were complying with their Maastricht treaty obligations. The essay’s conclusion was that the Euro would not long survive its first test.

History clearly demonstrates that a monetary union will fail without a common political will. The Euro has never achieved political union.

So the Euro stumbled through the private debt crisis of 2008 on the back of central bank largesse. This lead to a global socialisation of the massive debt binge that had accumulated through the mortgage and credit markets. But while the ECB. like most its central banking peers, was busy engorging its balance sheet there was still no consolidation of debts, each nation-state remained it own issuer.

So now we are approaching the next debt crisis – a result of 10 years of zero interest rates where the private sector has been encouraged to re-leverage, valuation multiples are back to all-time highs, and now the central banks are carrying impossibly large balance sheets. The sensitivity of the system to rising interest rates is acute.

And we have a Euro-land dominated by one very large creditor and quite a few large debtors. To date, the creditor has won out, forcing ‘internal devaluation’ on their partners. But the next stage of the sovereign debt crisis is unlikely to play to that script. You can already see capital flight out of Italy through the Target2 balances. The only palatable way out of this mess is to inflate your way out of debt – and the Italians have the keys to that locker. Beware the vagaries of capital in a hurry to find an exit…

During this new stage of the depression, the refugee gold and the foreign government reserve deposits were constantly driven by fear hither and yon over the world. We were to see currencies demoralized and governments embarrassed as fear drove the gold from one country to another. In fact, there was a mass of gold and short-term credit which behaved like a loose cannon on the deck of the world in a tempest-tossed era.

Herbert Hoover, Memoirs, Volume II, Chapter 7, page 67

My cooperative is ikigai

My cooperative is ikigai

So I’m in the ‘other’ demographic – the over-50 category – the one that has only one book-end. My teenage son offered some consolation, “No dad – you are in the 40 to 65 bracket, next comes the Integrity or Despair stage…”

I was introduced to the concept of Ikigai this week. It’s our reason for being.

It’s not a big word. It’s used in everyday conversation in Japan. “My dog is ikigai”, or “Mini-me you-complete-me” (if you are an Austin Powers fan). It is understood as a lived experience where the strands of your being are in harmony.

Being a little prone to extremes, I’ve explored the outer edges of my venn diagram. The “What you can be PAID FOR” is the trickiest to navigate. Money can confuse things.

One of the challenges with a coop is that the mission can dominate. This creates an unsustainable model for many a coop as it assumes that those involved can survive without compensation.

We need our cooperatives to be ikigai.


 

 

 

Governance with a token

So bringing some recent threads together – this video includes a discussion about crypto currencies and governance:

  • Taking a lead from game theory – let’s say that the rules of the game define how economic value is shared across participants, and that governance defines the way these rules can be changed.
  • In the capitalist economy – money facilitates the transfer of value, the profit motive determines how value is distributed, while the power to change the rules resides with capital (ie. shareholders).
  • In the crypto-space – we can have tokens that combine value transfer with governance such that the more people that use a system, the wider the distribution of tokens and the more people that have a say in governance.

Suggests a value-in-use approach to governance where you can “distribute power more evenly across the network because everyone is sharing the same asset” – as currency and capital are combined. Power is distributed based on how everyone is contributing to the network.

Under this model, the aim is to reward participation with the power to govern. Governance becomes a mechanism to protect against being “forked to death” as the value to govern accretes to those that work within the structure. A well designed governance mechanism will therefore better enable a network to evolve over time.

Also, suggests that models that reward participation are likely to be more robust than ICO’s where external capital gets to buy power up-front – which just internalises the governance problems that exist with proportional shareholder models elsewhere.

Got me wondering how a currency & capital token simplifies the management of a mutual…


 

Co-operatively solving the prisoner’s dilemma

Co-operatively solving the prisoner’s dilemma

Listening to a back edition of Seth Godin’s Akimbo podcast “Game Theory & the Infinite Game”, it struck me that a co-operative is, by design, an attempt to collectively escape the prisoner’s dilemma.

To recap the parameters of the ‘game’, two people are caught red-handed for a crime. They are going to serve a year’s jail-time for this. But they are wanted for a bigger crime too. So they are each offered a way out of jail-time, rat on your partner for the bigger crime and you will go free but your partner will get 50 years. The trouble is that if they both sing, then they will both get 5 years.

The expectation is that the rational selfish response will be to rat on the other. In practice, humans have demonstrated a systemic bias to co-operate – we seem to understand that the pursuit of single-minded self-interest can lead to a bad outcome for all.

So now I have added The Evolution of Cooperation by Robert Axelrod to my reading list (here for the abridged version). This book reports on a Prisoner’s Dilemma tournament where he invited game theory experts to submit programs that would be paired off against each other to see which did best over repeated interactions.

“Amazingly enough, the winner was the simplest of all candidates submitted.
This was a strategy of simple reciprocity which cooperates on the
first move and then does whatever the other player did on the previous
move.”

He called it the Tit-for-Tat strategy.

By analysing the top-scoring strategies, Axelrod stated several conditions necessary for a strategy to be successful:

  • Nice – Almost all of the top-scoring strategies were nice, that is, it will not defect before its opponent does.
  • Retaliating – A successful strategy must not be a blind optimist. It must sometimes retaliate to avoid being exploited by others..
  • Forgiving – Though players will retaliate, a successful strategy will once again fall back to cooperating if the opponent does not continue to defect. This stops long runs of revenge and counter-revenge.
  • Non-envious – The best strategies did not strive to score more than the opponent.

Whether the players trust each other or not is less important in the long run than whether the conditions are ripe for them to build a stable pattern of cooperation with each other. The successful strategy learns through trial-and-error that it is better to co-operate than not.

Some lessons here for co-operative design!

What is good data governance?

What is good data governance?

Governance is a hashtag that has been growing in popularity.  So it’s great to see the coop sector responding with the BCCM’s impending release of Co-operative and Mutual Enterprise Governance Principles for the Australian co-op and mutual sector. And folk like Coop News dedicating a Governance Edition to analyse the subject.

We’re interested in how this emerging thinking applies to data.

Data governance is the mechanism through which values and expectations with respect to data can be translated into effective management practices.

Good governance is a function of the clarity of shared intent and trust in expected behavior. As Dee Hock writes: “This is not to say that contracts, laws, and regulations do not serve a purpose. Rather it is to point out that…rules and regulations, laws and contracts, can never replace clarity of shared purpose and clear, deeply held principles about conduct in pursuit of that purpose.”

By these definitions, good data governance is broader than legal frameworks or codes of practice – it bridges the gap between the expectations and values that folk have with respect to their data, and the mechanisms by which these are made tangible. It encompasses legal documentation and decision-making protocols that are agreed between the actors involved. And it includes the social interactions that enable these to be made real.

The Cambridge Analytics seraglio exposed the under-belly of the market that preys on our data. And the GDPR has done wonders for focussing attention on data management practices. Next step is to get some clarity around the governance practices that will lay the foundation for better matching of expectations with practice.

PS – An example of a emerging model for personal data, Sovrin have a pretty comprehensive Trust Framework