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Lifespan of Social Metrics

Unlike metrics of simple systems, the metrics of complex adaptive systems have a life span. They grow in value over time. And then they decay in value as well.

Take twitter. At first it was follower count – and that still matters some. But over time, people found ways to gain followers that didn’t coincide to the value that they provided, so it started to lose usefulness as an indicator of value from the tweeter.

Jane McGonigal, @avantgame, just posted a series of tweets about how Amazon rating system is being gamed.

Interesting, my book seems to have been targeted last year by some conservative group, individuals encouraged to post negative reviews

a cluster of extremely negative reviews with a conservative POV posted at the same time with weird (untrue) criticism of my biography…

here’s a recent NYT article on partisan groups attempting to one-star bomb books on Amazonnytimes.com/2013/01/21/bus… …

“Here’s what I do: I go to Amazon.com + search for ‘liberal book’. I give 1 star, 1 star, 1 star”youtube.com/watch?v=tGB8Uu…

“Then I search ‘conservative book’ and give 5-star, 5-star, 5-star.” From a tea-party Internet training meetup youtube.com/watch?v=tGB8Uu…

apparently one trick is to purchase book so your review appears “verified”, then cancel order before books ships

Note: I took out a few tweets in the series which were just about her work, rather than about the gaming the stars method she points to.

Knowing that people are giving a count of stars based on their ideology rather than the quality of the work, I am now less likely to put stock in the star count on a given project, especially when it is politically polemic material. Thus the usefulness of the stars decrease for me (and for others) and then for the whole system.

With twitter, we first looked at “follower” count, and then that was gamed. Then the metrics had to start considering other factors like RT count to demonstrate influence. Now we rely on twittergrader, kred, klout, etc to take a great number of factors into weighted consideration to produce an “influence” or reputation score. This amalgam of factors evolves over time.

You might think this is just the way of social media, since it is a fast feedback loop. But actually, I think that a lot of measures of complex adaptive systems work this way.

What comes to mind is Scott Nelson’s story from Blockage in the Thrivability Sketch.

…It was during the crisis of 1857 that the previously ignored insights of a long-haired mathematician, abolitionist, and utopian socialist named Elizur Wright were finally recognized as critically valuable for economic stability.

In the 1840s and 1850s, Wright had tried to convince the state of Massachusetts that life insurance needed reform. As a mathematician, he had been asked calculate the present value of any given policy based on the premiums paid in, a calculation that British mathematicians had called impossible. He created a rule-of-thumb called “net present value” (NPV) to determine the value of a flow of resources in a single instant (present value) and then to subtract operating costs (net).

But the more Elizur calculated, the more troubled he became. Many companies by his calculations spent so much on advertising that they could never pay off their policies. Others profited by canceling policies for those who missed a single payment. The effect was often to end a policy a year before death, leaving families with nothing. Wright fumed, but in vain. In the go-go 1840s and early 1850s, no one would listen to his criticisms and only a few would accept his principle of valuation. But through the 1850s he returned to the Massachusetts legislature with a blueprint for reform. When the Panic of 1857 hit with the failure of a bank called Ohio Life Insurance and Trust Company, Elizur was prepared. This blockage of trade and transport, Wright declared, was a result of distrust. Insurance companies needed reliable accounting practices that would allow Massachusetts to calculate net present value, and internal rate of return. When trust returns, Wright assured them, the blockage will be over.

Unconvinced but without options, Massachusetts adopted Wright’s blueprint, preventing any company from selling insurance in Massachusetts that did not provide complete financial information. NPV offers transparency of obligations.  The panic was short-lived, and Elizur Wright’s accounting principles became the basis of what we now call Generally Accepted Accounting Principles, adopted by millions of companies, states, and non-governmental organizations throughout the world. MBAs take credit for it, but a long-haired radical gave us cost accrual accounting.

Wright took advantage of blockage to identify its root cause – a distrust of opacity. Increased financial transparency was the solution; trust collapses without it. Blockage can let us make institutions open up and make them thrivable.

If the metrics we use in our economy are also being created (even at a very slow pace) they may also be declining in usefulness. Elizur’s methods didn’t anticipate the complex financial instruments to come over 100 years later that obscured the “trustworthiness” of the things our measures aim to reveal.

Consider how the measures you use can be gamed, where they may be in their lifespan of adoption and decay, and what other indicators might be emerging to reveal what matters – the territory and not the map.

Strange Attractor Design

We had a series of aha moments. Herman was explaining a recent design choice. We connected it with a prior design choice, and this took us to a transcending moment of seeing how these patterns are at work. This unleashed a raw flurry of cascading aha moments, which are roughly captured here.

Designing Networks

Consider a group of businesses and organizations that are all connecting to each other. Very quickly, the amount of information being tracked about all the others in the network overwhelms the nodes. (Herman drew the actual network math, I drew a dense network cluster.) The orgs have information that they need to exchange in order to unlock value that they benefit from. So, how do they reorganize the network to enable the optimal information flow patterns in a network that has limited trust between selfish nodes? So often when asked to design these things, people think in terms of idealistic utopian design conditions. It is about how much functionality can be given so that any information can flow in any direction. However, all the nodes find this scary. Instead of designing for optimal participant conditions, design for “willing to cooperate, yet anxious” instead.

As a network solution, you create an intermediary to reduce the network connection count. If I know the right network members, I have 2 degree access to everyone while not having to know most of them. So, see my “count of nodes” divided by “count of hubs” which is a rough way of saying, in back of the napkin kind of math, that each intermediary is reducing the number of nodes you need to directly connect with.

Trust

The next problem is why would we trust this intermediary? Again, lots of design decisions are made for central control of these hubs. What Valdis calls the “queen in between” kind of model. Most of us are skeptical about the power that aggregates in these hubs. So we need ways to ensure they are reliable. Solution: a) limit what information does flow through – only a certain type or amount. b) make all the nodes equal in some way and c) make the flow of that information extremely transparent.

When we don’t have much trust in the health of the network, the other nodes in the network, or in intermediaries, we have to manage high amounts of information about all of them. We act paranoid. When we have significant trust in the health of the network and the nodes within it and the intermediaries, we act generously. We can handle more “hops” between members, since the trust is in the network as much as it is in the nodes of that network.

One of the aha’s here for me was actually a reminder more than an aha. It is not the FORM of the network that determines the way it needs to be governed. It is the evolution of that network that determines the governance. How did it mature? If trusting in the past has led to positive outcomes, more trust will continue, and less governance is needed. However, if trust has been broken, more rules might be created to qualify when and how to trust, and thus bureaucracy begins to flourish. But I will get back to that later.

Where you see the figure 8 on the side, I am explaining to Herman about polarity management. We are not just greedy and selfish nor are we just open generous cooperators. We exist in the tension between these extremes. And even the skeptics are willing to navigate the risks of cooperation when the benefits might be higher.

Incentives

Herman mentioned how the use of financial incentives can often be misleading. The amount of the financial incentive is not what allows the node to participate in the network. Instead it is the ability of that node to maintain identity with their peer group. Can they save face and have pride while taking the risk of cooperation? Yes if there is some token amount of money involved. Or it can be another kind of token.

We have another case where credits were used between players to negotiate their placement in a queue. I might let you pass me in line, if you give me your credit. I can still save face to my peers for letting you cut, because I got some benefit for it. This design may require an arbiter to view the transaction, but the arbiter isn’t needing to make judgements about whether it can happen or not. Tokens can make visible an exchange that allows both sides to save face to others and enable actions in cooperator networks.

choice

Identity

We find the issue of identity to be a huge factor in the behavior of nodes in a network.

We talked about how a transaction between two people always seems to have at least a third party. I am not only negotiating with you for what the transaction is, I am also thinking about what my group or tribe will think of me for engaging in that transaction. In fact, I may have more anxiety about what they think than about whether you are making a good transaction with me or not. Most of us dedicate most of our time to identity formation, with everything we say and do designed, intentionally or not, to reinforce what we want to think of ourselves. We may not do a good job of it all the time, but that is a driving force.

Choices

I told Herman about a conversation I had with Benjamin Ellis about making choices. Benjamin was talking about a graphic designer that provided 3 choices. And we quickly riffed on why this works so well. We think that the selecting out of the bad option of the three builds the sense of confidence in decision making that enables the next choice between the remaining two to be faster and seem easier.

Rules, Rules, and more Rules

We also talked about how the centralized control design model has these negative side effects of generating bureaucracy because they tend to create rules to follow, and when those rules conflict, they create rules about the conflict, in an infinite cascade of rule making that ends up grinding cooperation to a halt. Thus we said they are limited in size by the ability to moderate the rules. This did help us transcend the 150 limit of community cooperation (limit to the ability to track the trustability of members of a network with each other). However, when you design for robustness (bookmark conversation on resilience, robustness, and anti-fragility)… when you design for robustness, you put the value and power at the edges of the network operating on principles instead of rules, and allow it to learn and grow from a simple structure, you get agility and adaptability in the network…and it can become scale-free? I think.

Optimize for what?

We moved to a meta-level discussion about how any group tends to design to optimize for one factor. When we succeed at that optimization, we also learn that there are negative side effects that may inhibit our achievement of another (possibly more important) goal. So we have to switch to design to optimize for something else. This can be long term human social evolution at work – when we exhaust the potential of something, we have to evolve to work around that. This often involves making distinctions between things that were previously hooked together in order to unlock value… but I digress. Will save that for another conversation…. back to the design at hand.

Rituals

In talking about Identity, we had taken a side track to talk about the power of ritual. I had shared a story about being uncomfortable – with a growing sense of dis-ease when I was in a group that had a series of actions that were meant to create bonding in the group culminating in a big ritual together. Rituals can help ease the anxiety of cooperation by bonding everyone into a peer group – we are all smart or stupid together, so I can work within that group easier. Everyone has crossed a threshold together. And you feel it in an embodied form. However, we need to take into account that humans have a spectrum of tolerance to peer influence. Some of us just don’t care what our peers think. And others can’t make a decision without being aligned with the group. It isn’t a stable known to design for.

There is that formula that Herman wrote. 🙂

Design to Evolve Cooperation

In pink at the bottom it says “It isn’t the form==> governance. It is TRUST==>governance.”

On the left in blue it says, “Design big” from the start and you get “bureaucracy, low trust, not agile, ultra specific and rule based.”

If you start small, simple, and mature through the growth of trust, you get evolving cooperation. What is the least that is needed? What is the smallest channel that enables a flow of information that benefits the network? Design for learning and self-evolution. Design to grow using minimal rituals for a foundation of trust to help mediate the anxiety of cooperation. When evolving cooperation against the will or inclination of players, manage their fear and sense of security. Sometimes tokens or credits can be used to mediate that trust.

I am very interested in how tokens are used to engender trust and enable flows. How does a token act as an object of trust transfer? Trust tends not to be transferable, whereas tokens can be. How do you route the trust into the token and the process, so that it can be transferred?

This is where we start really transcending that 150 limit.

 

Sorry this isn’t an essay yet. It is just notes from an extended conversation.