What is the atom of labor?

My colleague, Betsy Masiello, already postedthis weekend on the future of work. I concur with her reading recommendation.

In addition I just finished Jim Clifton’s The Coming Jobs War. It is a thought-provoking book about what people want and what is needed, for the US, to beat the prediction that in 30 years China will have the largest GDP in the world. Clifton argues that what everyone wants, across the globe, is “a good job”.

A war for good jobs where none are to be had?

He defines this later as a formal job with a good company, that employs you 30 hours a week at least and pays the bills. But also as a job that makes you feel engaged and valued. A very reasonable ask in a sense. But there is something that worries me about the framing here.

I think Clifton essentially has hit on one of the biggest challenges in the next 30 years. But it is not the challenge he believes we face. It is not the challenge of creating, as he says, 1.8 billion jobs. Or 10 million in the US. It is the challenge of explaining why the concept of a job is changing.

My generation has been very lucky, and I myself am very fortunate in having a “good job” as defined by Clifton. “Good jobs” represent the top accomplishment of the industrial economy, and the company such as we know it the undisputed engine of that ecopnomy. But what if that is changing over the coming 30 years?

After all, the notion of what a job was in agricultural society was radically different than that we now have. Why would jobs stay the same as our society changes production patterns and economic foundations shift?

Now, don’t get me wrong. I don’t believe in jobless growth. The amount of work is not decreasing, but the chunks of work are getting smaller, shifting. In itself this is hardly surprising, nor a new insight. Part of Adam Smith’s genius was to see that division of labor is the key accomplishment of technology, and that we are dividing work more and more over time

From the 70s to now we have thought of this as specialization. We have lamented the heavy specialization of the economy, since it makes it brittle and difficult to retrain. But specialization is only vertical division of labor. We still end up with chunks of work that lend themselves to “real jobs”. What if technology’s next move is to horizontally divide labor, so that we have even smaller chunks of work to go around?

Some can be outsourced, some not, but the chunks get smaller. The possible development here is that as division of labor continues the resulting chunks are not equal to what Clifton envisions would fill a “good job”.

A “good job” is not the atom of division of labor produced by the on-going technological revolution.

What is the smallest chunk of work we can imagine? A processor cycle? What is the atom of labor? We don’t know. And whether the universe of work will still be divided as to allow for a jobs structure that we have now taught everyone to desire seems to be an open question as well.

Clifton’s book becomes a brilliant example of how important real jobs have become to our civilization, at perhaps the moment where they will no longer be a meaningful way to organize the market for work.

Don’t get me wrong. I am still an extreme optimist when it comes to the future (and some will rightly laugh at that and say that it is because I have a very good job indeed), but I believe that whatever the future market for work looks like, the bundle offered as a “good job” will become more scarce. And acquiring the skills to act in that new market will take time for everyone.

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What Style of Artificial Intelligence Do You Want?

Imagine that we succeed creating artificial intelligence. What will that mean? Will we be able to buy an intelligence and then put it to use for our purposes? And I am not even talking about the vaguely slave-like associations this brings to mind, I am thinking of intelligence as such. Will buying intelligence be like buying a commodity or will it be like buying branded goods? Will there be a fashion in AIs?

Here is what I am thinking: as we get closer to artificial intelligence it seems clear that we are defining it narrowly, we seem to look at human equivalence tests, like the issue of if an AI can outperform a human. We do not ask how an AI outperforms someone. The reason is that we often seem to try AIs against humans in situations where there is one right outcome. The AI wins or does not win the chess game. It wins or does not win Jeopardy. It drives safer or not. Well, can an AI play a certain style? Not imitate, but play a certain style of chess, for example. Is the style of a certain AI chess computer more like Kasparov or more like the defensive player like Tigran Petrosian?

So back to the question above. Will we have fashion in AIs? Could neurotic, slightly irritated AIs be in fashion one year, and subservient sci fi AIs another year? Douglas Adams famously equipped his robots and AIs with personalities, but what if the personality is just an emergent property of the kind of intelligence we have generated?

I tend to think that is the case. Personality is a flavor of intelligence, and we could imagine a world in which markets in AI competed basically on what different kinds of personalities the AIs exhibited. You could have minimal personality AIs or you could have outrageous AIs compete on the basis of what interaction style people prefer.

So imagine a review of AIs fifty year down the road, with the mandatory comparison table between the two new brands of AIs. What will the comparison rows be? Maybe the different AIs will be graded on things like:

  • Rationality
  • Humor
  • Knowledge
  • Compassion
  • Voice
  • Gender(s)
  • Creativity

That is one future. The other would of course be that we use a system like this that is already in existence for the comparisons: the dating sites. Romantic engagement with AIs may well be the natural endpoint of a century obsessed with relationship issues. In a sci-fi book I recently finished the author commented briefly on the introduction of real AI, and said something like this: marriage rates plummeted and society almost dissolved and never recovered from its loss of focus on the family. And it was not even because of sex robots, no cyber sex, just the fact that they could find a much closer fit when it came to someone to listen to them, advise them and understand.

There is a future in which you get your Engadget reviews on match.com.

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A home is a computer running an interior decoration script

The growth of an internet of things, sensor networks and ad-wireless networks will lead to interesting innovations. One of the things that I am eagerly awaiting is designed  and authenticated environments. If you add biometrics to the above list of trendy things you see how that could happen. A quick biometric scan when you enter an environment and suddenly the environment is looked up with an environment service provider and everything, music, sound, light and temperature is adapted to you.

Current scenarios for ambient intelligence are more about what machines work together, but it seems far more interesting to think about what different environments can be instantiated around an individual in general.

Or negotiated with others — and here you could imagine an enormous amount of weird protocols, and not all of them pleasant. Imagine a world in which you pay to impose your environment, and so the person willing to pay the most would be able to impose his or her environment on everyone else. You would literally know when someone richer – 0r more willing to pay – than you entered the room, because the room would change.

You could also imagine using other cues, like klout or social network centrality. And then, of course, the more brand aware of us would construct distinct environments to go with them. I would totally subscribe to a spooky enviroment. Lights go down, Ligeti is played and it becomes significantly cooler…

Adaptive environments are not far away, and they will boost a boom and a new sector in interior design. It will be mobile interior design, in a way. And homes will be general purpose computers to run interior scripts on. (On a more serious note this is something that is already discussed by researchers helping others to overcome different disabilities, and an important field for research.)

An interesting question emerges from all of this: what around you could be a computer and what would it mean for it to run software? A home is already a computer running a very slow interior design script in one sense. Just increase speed and capacity and you get the mobile, ambient, intelligent and adaptive (buzzword bingo!) environment.

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Regulatory complexity and reform costs — take two

A recent paper, “Network Analysis of the French Environmental Code”, available here, analyzes links in French regulation, finds a rich club of ten codes often cited by all others and shows what regulatory complexity could be interpreted as.

I wrote earlier about reform costs and I believe that regulatory complexity and reform costs are tightly connected. But reform cost is harder than just stating the complexity of a regulatory network. If the network we are discussing has ten major codes that all are referenced too, changes that pertain to them are actually cheaper — so the really high reform costs will occur in regulatory networks that do not exhibit power law structures, or where the core sets of regulation are harder to change. One way of thinking about any regulatory network is that the more connected a law is the harder it is probably to change it – the constitution is harder to change than an environmental protection agency’s guidelines – so we need to measure both regulatory “rigidity” and connectedness to figure out reform costs over time in any arbitrary regulatory network.

The topology of a regulatory network, generally, is a fascinating subject for research. I hope to see more research like this, and one question is if citing another code is the right way to craft edges. I imagine that you could also think about what laws are dispositive and what aren’t, how they relate to each-other and what the scope is.

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Hur räknar du poäng?

En diskussion nyligen diskuterade jag hur man kan använda spel för att förstå sin verksamhet bättre. Det handlade huvudsakligen om olika spel som kan användas för att skapa idéer och insikter, men det är också möjligt att förstå sin egen verksamhet som ett spel. Jag funderar på vilka frågor som skulle vara intressanta att ställa sig. Här är listan jag har skapat hitintills.

  • Hur räknar du poäng? Det vill säga, hur vet du att du leder, vinner eller ligger under i ditt dagliga arbete?
  • Hur ofta delas poäng ut? Olika jobb erbjuder olika sorters poängmodeller: det kan vara veckomodeller eller dagliga modeller eller andra cykler, men vilka cykler ditt spel arbetar med är viktigt. (Det går också att tänka sig att det finns en skillnad mellan turn-based och real time).
  • Vilken sorts spel är det? Wittgenstein påpekade som bekant att spel är ett begrepp som endast kännetecknar en familjelikhet i en stor kategori företeelser. Så, frågan är om du spelar schack eller go eller boxning.
  • Hur ser balansen ut mellan aleatoriska och agonistiska element? Hur mycket beror av motståndaren och hur mycket av slumpen? Begreppen agonistiskt och aleatoriskt stammar från Roger Callois arbeten om spel, om jag minns rätt.

Alla tips på andra frågor som man skulle kunna arbeta med i en session med sitt team uppskattas! Jag hade en lång och intressant konversation med en av gamestorming-grundarna i dag, så jag borde kunna komma fram till en modell för hur jag vill göra detta, eller hur?

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Farlig forskning

New York Times angriper i en ledare forskningen om fågelinfluensa. Ett forskarlag har lyckats skapa en variant av fågelinfluensan med samma höga dödlighet (nästan 50%) som smittar via nysningar och hostningar. Borde de ha fått bedriva den forskningen? NY Times skriver:

We nearly always champion unfettered scientific research and open publication of the results. In this case it looks like the research should never have been undertaken because the potential harm is so catastrophic and the potential benefits from studying the virus so speculative.

Och visst är forskningen skrämmande. Den visade hur enkelt, med blott fem mutationer, viruset kan förvandlas till ett luftburet hot mot mänskligheten. Så kanske borde vi förbjuda den här typen av forskning. Men det finns två principiella problem med den ståndpunkten. Den ena är att det förutsätter att det finns någon som får bestämma vilken forskning som kan bedrivas och inte bedrivas och det andra är att det förutsätter att forskningen inte har något värde.

Det första problemet kan kanske åtgärdas genom att vi sätter upp etiska kommittéer av olika slag, men det är ändå en risk att inskränka forskningens frihet på det sättet. Och var drar vi gränsen? Om det med tre av varandra oberoende forskningsinsatser, var och en ofarlig, går att förvandla ett virus till ett vapen — ska vi då förbjuda även dessa insatser? Visst, det kan tyckas vara ett teoretiskt problem – vi talar om ett mördarvirus! – men det är inte oviktigt.

Det andra problemet är mer intressant. NYT är snabba att förklara att nyttan är spekulativ. Men stämmer det verkligen? Den som vill försvara forskningen kan säga att vi nu vet hur otroligt lätt fågelinfluensan muterar till ett dödligt hot mot oss alla, och att det visar med all önskvärd tydlighet att vi borde försöka hitta nya medel mot virusinfektioner. Är inte forskningen i själva verket en väckarklocka för forskningen och samhället? En signal om att vi står närmare en pandemisk katastrof än vi trodde? Och rätt tolkad skulle väl sådan forskning kunna rädda miljontals liv? Och vad lärde sig forskarna egentligen om hur virus muterar när de gjorde sina experiment? Tänk om det finns kunskap där som verkligen kommer till nytta när något virus muterar i det fria?

Det kan vara frestande att förbjuda viss forskning. Vi har ett dubbelt förhållande till kunskap, och fruktar fortfarande viss kunskap som om vore den nästan teologiskt förbjuden. Men i valet mellan att veta och inte veta är det oftast bättre att veta. Även om det vi vet är skrämmande.

Motargumentet är givet: vad händer om någon nu stjäl det här viruset och släpper ut det? Hälften av alla människor på jorden skulle kunna dö! Hur kan någon försvara det? Och risken är inte försumbar heller, det skulle verkligen kunna ske. Problemet är att om vi inte vet hur lätt det är vet vi inte heller hur lätt det är för diktaturer och terrorister. Nu vet vi det. Och vi har anledning att ägna betydande uppmärksamhet, resurser och insatser åt mer forskning – inte mindre.

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What is your care/share-ratio?

This article in MIT Technology review made me think. The author sets out comparing Moore’s law and Zuckerberg’s law and notes that there are fundamental differences between them. Z’s law says that online sharing will double yearly over the foreseeable future. The article author notes that this is not interesting in itself without an additional concept of “caring”.

We could simplify this and think of the caring/sharing ratios for different network: how much material we share and how much we care about or actually consume. (Presumably we could even define social networks as networks that depend on their care/share ratio for survival long term). As the c/s-ratio approaches zero there is a point at which people just quit.

That allows us to ask a couple of interesting questions about social networks:

1) Is the c/s-ratio dependent on the number of connections or other network properties? I.e. is there a social density relationship? This seems almost certain, but I am not sure what the relationship looks like.

2) Is the c/s-ratio dependent on social segmenting (lists, circles et cetera)? Does social segmenting help us care more?

3) What is the unit of measurement? Time spent caring/sharing? Or mbs shared/cared about? One way to understand Zuckerberg’s law would be to say that it applies to the amount of data shared, as in the size of the shared data set. That will grow as we share more high-resolution images, and that will happen as laws like Moore’s increase capacity of technology. But if I share a 1 mb picture one year and a 2 mb picture next year — is it really accurate to say that my sharing doubled? It seems clear that Zuckerberg’s law was conceived based on mb, not hours. Therein lies a lot of the challenge to it.

4) Is the c/s-ratio applicable to other online phenomena like gaming? I share and care at the same time when I play WoW, right? It seems that there is something asynchronous going on in the c/s-analysis.

5) It seems obvious that the c/s ratio will develop in a certain way over time, and that it will compete with other things we care for. Attention economics needs to be developed more in detail. Is there any way that we can increase our attention? I think visualization – as the article author hints – maybe really important here. Applications like Wisdom allow us to “meta-care” about things aggregated into visualizations.

6) What does the c/s-ratio look like if we order the networks we participate in today? 4sq, FB, Twitter, G+…I think my order is G+, FB, Twitter and then everything else (but of course I am biased). I guess my email has a c/s-ratio too. Hm.

There is a lot more (some notes also here), but those are some initial thoughts for now. On a not so related note there is snow in Sweden, and everything is very christmasy. It is kind of nice to have snow in December. I did not think I would feel this way.

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Argument from different model – a new foul in the theory of argument?

In Dietrich Doerner‘s excellent book on decision making, charmingly called the Logic of Failure, he develops the following problem solving model:

  1. Formulation of goals
  2. Formulation of models AND gather of information
  3. Prediction and extrapolation
  4. Planning of actions, decision making and execution
  5. Review of effects and revision
This is a great model for several reasons, but the most important thing that Doerner has in his model that is left out of many other models is the first element in the second step: the formulation of, exactly, a model. It is essential to problem solving, and far too little time is usually spent on it — because it is hard.
So what does it mean to develop a model? Let’s look at a practical problem. Let’s assume that I want to win an election. That is my goal. Now, what has to happen next is not that I run out and look at stats and opinion polls and good knows what, but rather that I agree with my team on how elections are won. What is the model we are working with? Do we believe that the electorate consists of three different categories of votes, for example? Right, swing and left? If so, do we care about the left and right or only the swing vote? How do we think someone decides if they are a swing voter? What matters most? If one member of the team thinks it is about the appearance of winning (i.e. the sharp jaw, the great hair) and another thinks it is the issues, and specifically the economy their gathering of information and planning will look very different. If it is a little bit of both, maybe that is a good thing to agree on too.
A weak model will almost guarantee a weak outcome. If you want to beat a chess master or a karate fighter your model will be chess or karate as a formal system, and you will try to figure out if they favor an opening or a special kick through gathering information. You will not expect the chess player to kick you, or the karate fighter to move his queen.
Yet still many people have only very hazy models of what the game they are playing looks like. So a new generation of problem solvers have recommended that you think about your problem as a game. What does playing the game look like? How do you win? How do you keep score? Any more complex problem deserves being turned into a game if for no other reason than the utility in showing you your model, and examining it with others.
Whether you do it by formal gamestorming or just by thinking about this as a systems science challenge (as in systems thinking) does not really matter, I think. But being explicit about my models is one of the things I am working hard on – and I think an exciting thing to think about.
A classical example of model failures is perhaps found in politics. It is the case where someone believes that they are right on the substance, and cannot understand why the system still comes to the wrong conclusion. When that happens frustrations ensues and people start calling each-other names, claim that they are irrational or evil. But that is rare. It is far more common that people have different models of reality than that they share your model and are evil.
Model disconnect is probably a very common phenomenon, and a very upsetting one. I can think of several examples of this in economics, climate change and information policy that would do well from recognizing that.
Perhaps we should introduce a new foul in the theory of rational argument? Just as we have outlawed argumentum ad hominem we could introduce argument from different model as a foul – but for both parties.
If they do not agree on the model, well, then they are not really arguing at all. They are just loudly misunderstanding each-other.
There is a lot of that, though, I fear.
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The imperative of openness for data society

Aficionados of Isaac Asimov’s Foundation series will like this article from MIT Technology Review. In the article we can read bout how physicists have developed methods that allow forecasting, much like weather forecasting, of purchasing decisions. And the way they do this should sound familiar to Asimov fans:

Here’s how they do it. These guys think of humans as if they were atoms interacting with each other via three different forces. The first is advertising, which they think of as a general external force, like a magnetic field. The second is a word-of -mouth effect, which they model as a two-body interaction. Finally, they think of rumours as an interaction between three bodies.

The possibility of predicting the systems as the number of forces working on the atoms increase is of course the really tricky question. Asimov also used the analogy of a gas and establishes a number of important theorems that form the basis of psychohistory. One of the most important ones, but one that I believe is often excluded from enthusiastic discussions of how we could use data sets to predict different things, is the second (or third, accounts vary) theorem of psychohistory. It says:

that the population should remain in ignorance of the results of the application of psychohistorical analyses

I have seen people react differently to this idea, that the usefulness of a predictive system declines with how well the predictions are known, but most reactions seem to want to trivialize the problem. Yes, people will say, but let’s try anyway. I think that is a good attitude, but it is theoretically interesting to examine what it would mean if we move down the road that research like the one referenced in the MIT Tech Review opens up.

In one sense psychohistory is a manipulative tool. A society predicted loses some, perhaps all, of its democratic nature, and we move from a free market to a more planned economy. Hayek famously argued that the knowledge coordination problem is not only best solved, but only solvable, through markets, but there are those who believe that modern technology proves him wrong — that we could predict and plan society much better now with the large data sets that are accumulating everywhere in our society. They are not necessarily wrong, but their conclusion, per the second theorem, depends on those data sets and predictions being kept secret from others.

But, they argue, that is fine. Because the value of living in a carefully predicted and planned society is larger than the value of living in a society where everyone has access to the data used for the predictions and where these predictions are thus rendered useless. Their arguments will range from national security, health, economics to social equality.

Let’s assume for a moment that the complexity facing any attempt at psycho-history is not such that it renders all attempts futile. That we could in fact develop social predictions with high accuracy through the use of large data sets, and that we could act on them and plan our societies accordingly. The question we then have to ask ourselves is this:

Does the value of predictability trump the value of openness?

There is an assumption here that is worth highlighting. And that is that for a democracy to remain open it can not be predictable by only a few. That is a complex and perhaps provocative assumption that I think we should examine. I believe this to be true, but others will say that our democracy already is predictable, in some sections and instances, only to a few and that they build their power base on that information asymmetry, but that it is reasonably open still. Maybe. But I think that those asymmetries are not systematic to our democracy, but confined to those phenomena, like stock markets, where they are certain to be important, but where they also do not threat the nature of democracy as such.

In summary, if we share the data and allow everyone to use it, then predictability goes down. And I think it decreases fairly fast. So in a simplistic graph:

This leads to the guess that in the information society, open access to data is not only a great way to encourage new entrepreneurship, it is actually also a safe-guard for an open democracy. Governments are still, by far, the largest data holders, I think, and the data they collect is very suitable for social predictions. (And yes, companies should do this too. We do, and others do too, through tools like trends and insights, and we believe that the world is better for it, with lots of research flowing from that shared data).

I remember reading Asimov, and re-reading him, with awe. I loved the idea of psycho-history, and though that if only the math-geeks were in control then things would turn out just fine. I thought Asimov himself was a prophet. Turns out that he was quite sceptical, and often talked about both the advantages and dis-advantages of being able to predict societies and plan them.

In fact, in Asimov’s later writings it turns out to be a robot that encourages Seldon to develop his science into a social means of control. As we all know, the robots in Asimov operate under the three laws, and they want to reduce harm to humans. A predictable society would allow for that to be done as carefully as possible, but it would also curtail some of the human spirit, creativity and holy insanity that make us human.

If there is a conclusion here it seems to be to explore the amazing value of data under the imperative of openness to the the full extent possible to ensure that our societies gain from this new, fantastic age of data innovation, discovery and exploration that we are entering into, but never compromise on that openness in the pursuit of macro-social predictability.

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At what reform cost does democracy break?

Imagine that a lawmaker came to you and said that he wanted to propose a law that would contain the following provision:

This law can only be changed after 15 years of complete consensus in the legislature and after paying 10 million USD.

What would your natural reaction to that provision be? I am guessing that it would be mixture of incredulity and horror, right? Because if we start legislating like this we completely invalidate the democratic processes that are enshrined in our constitutions.

But all laws are a little bit like this. To change a law is always harder than it is to put it in place. Revoking a law is very, very hard, since we believe that there is some wisdom in evolving the institution of the law respectfully (an idea that I agree with, to a certain degree – I do think that society goes through periods of punctuated equilibrium where it is appropriate to change and revoke many laws, but that is another matter).

One way to understand this is to think about it as a matter of cost. All laws have, because of the system they are embedded in, a certain reform cost associated with them. A number of observations flow from this assumption.

First, that not all laws have the same reform cost. Laws that are embedded in systems with greater inertia have a higher reform cost than laws that are more or less isolated in national legislation. A simple example would be a European directive vs a national law in a non-harmonized area. The European directive takes longer to change, and the cost associated with reforming it is greater than the cost of changing purely national legislation.

Second, the order of magnitude of reform cost goes up for every system that the law ties into. Let us define a way to measure this. Let u be the reform cost of a law. Here is my proposed hypothesis:

u for any given law can be calculated as the cost of changing the law of the different independent regulatory systems it is embedded in.

This is somewhat unexact, and only works as a guiding principle, but let’s take an example. The European directive on electronic signatures has been embedded in 27 systems, so the reform cost for that is 27 times what it would cost to change a rule that had only been embedded in a single country’s legislation. But, that does not really capture the complexity of different levels in the regulatory system, right? A European directive has to be implemented in national law, so changing it is a twofold process – first change the directive, then implement across 27 different countries. I think that deserves another multiplicator. So we end up with the notion that the reform cost depends on the number of regulatory systems that need to change times the number of actual regulatory levels (international, regional, national). That seems to give us an interesting way to extend the hypothesis. So.

u of a law is the cost of reforming it times the regulatory systems it is embedded in, times the levels of regulation that come into play.

In the EU case we would get 27*2*reform cost. The number of countries times the number of levels times the cost. We could always throw a national constant in there for specific cost adjustments in some countries. So if a country just accepts EU directives without implementation, well, their constant would be reflect canceling the lions part of the two-level cost.

So, why is this interesting, then? Very legitimate question (and thanks for staying with us this long!). The reason it is interesting is that I would argue that very high reform costs undermine the legitimacy of not only legislation but democracy. Just as in the example, we start thinking that this particular legislation breaks democracy since it is so hard to change it. Why bother to engage?

Now to the next observation. As we go towards a more and more globalized context, our legislation accrues higher and higher reform costs. A law flowing from an international treaty, embedded in a directive, implemented in national law and protected through a trade agreement has four levels of complexity, and if it is implemented in any number of countries, well, reforming it is almost impossible.

Should we allow for that?

Well, you may argue. We arrived at those laws in legitimate ways, so why not? If a law is decide upon in a legimate way, and put in place without force, it is legitimate. I think this is a flawed argument. The reason is really interesting. I think the legislative process is asymmetric: it is easier to produce laws than to revoke them. This asymmetry creates a situation where the sum total of democratically arrived at legislation may be undemocratic in effect.

Let’s say that again. The sum of democratically decided legislation is not always democratic in effect. In fact, there can be cases where it hurts democracy, because of exorbitant reform costs.

So what can we do? We need to start thinking about symmetry in legislation. How our processes for revoking and sunsetting legislation can be enhanced, how we can craft instruments for challenging sclerosis in the legislative system. We need to start measuring and carefully thinking about reform cost as a very peculiar cost on democratic and civic engagement. Reform cost is a powerful deterrent for anyone who is engaged in an issue. If I want to change something, and now that I face a condition like the one described in the beginning of this text, I will be dissuaded.

Activism, if we want it to be something else than empty posturing, needs to have a chance to succeed. Civic participation, for it to make sense, must make a difference. In a society with exorbitant reform costs none of that is true.

Let’s figure out how to change that. Maybe change begins by measuring.

 

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