The Personal Files of Matt Goldenberg

I write my thoughts here about Ed-tech, entrepreneurship, and systems of innovation.  You can read along if you’d like.

30 Day Challenge – Hacking Conscientiousness

Ever since I can remember, I’ve had a conscientiousness problem. I find myself for instance in a room of the house, wondering what I was originally planning to do there and grasping wildly around for context. Putting things down and forgetting where I left them. Locking my keys in the car. Missing meetings unless I have a physical alarm on my phone that reminds of them (and even then often needing multiple reminders to ensure that I don’t get engrossed in what I’m doing after the first one).

As I’ve grown and changed myself in innumerable ways, this has also been an aspect of my personality that I have hardly managed to make a dent in. Sure there are a few bright spots. I’ve managed to make huge strides in the goal directed part of my conscientiousness, significantly reducing my procrastination and becoming confident in accomplishing large and complex projects, becoming productive, focused, and directed.I’ve managed to change the dial on my “cleanliness” regulator to keep a much cleaner space. I’ve gotten better about putting every appointment in my phone calendar and carrying it with me everywhere, and I keep an extra car key in my wallet so locking my keys in the car is no longer an issue. But the basic “in the moment situational awareness” that is a huge part of what is commonly called common-sense has been stubbornly resistant to change.

Starting today, I’m embarking on a 30-day challenge to try to change that. Every morning I’ll be doing some offline deliberate practice of conscientiousness in different situations, like leaving my car, entering a room, or going to a meeting. I’ll be focusing acutely on what awareness feels like, and working to deepen and enhance my own understanding of awareness through techniques like focusing, anchoring, and association. I’ll also be focusing on some identity change work on any parts of myself that may cling to that idea of being an unconscientious person. I’ll be checking in here every Thursday to give an update on how it goes. Wish me luck!

Dealing With Uncertain Times (Effectuation and Antifragility) – Mental Model Monday #14


Today we’re going to talk about non-predictive control. Among strategists, I think predictive control often gets the most attention – with talk of superforecasting, prediction markets, and calibration dominating talk about decision making strategies. However, strategies for non-predictive control are just as important to an inspiring strategist.  How do you operate in circumstances of extreme volatility and uncertainty?  When you don’t know what’s going to happen next, how do you choose the best course of action?

Before we can meaningfully talk about how to deal with that type of uncertainty, we need to elucidate how to recognize that type of volatility, which we’ll do now.  Let’s go over the three types of risks that a strategist can face.

The Three Types of Risk


Certain Risk

The first type we will talk about is certain risk, is represented here by a clear bag with colored balls in it. Now, as long as we know how much the green, the red and the blue balls are worth, we know exactly, which ball to bet on.  We can make a simple expected value calculation of the value of the balls multiplied by the probability of getting the ball and that tells us which ball to take.

A traditional business plan assumes that you’re in a certain risk environment. You know exactly what to do. You just need to create a plan that’s going to consistently pull out the right ball for you faster than anyone else can get to those balls.

Uncertain Risk

The second type of risk is uncertain risk. This is represented by a a bag where we know there’s red, green and blue balls in it but we don’t know how many of each one. So, what we’re going to do is use sample the balls.  We’ll  start pulling a few balls out, making some small bets or just pulling without paying.  As we pull more balls out, we’ll start to get a sense of the distribution and once we’ve done that we can go back to the planning strategy of the certain risk.

This strategy in the business world is analogous to lean start-up and customer development techniques where you’re running small experiments with your business. You are making small bets. You are talking to customers to get a sense of the distribution of where the expected value is and then you move forward and create your company.

Knightian Uncertain Risk

Finally, there’s Knightian uncertain risk. The idea here is that we don’t know what color balls are in the bag. We don’t even know it’s balls or hubcaps or acid, and the contents of the bag are changing and shifting moment to moment.  In this case, the predictive strategies won’t work. The idea here is to use the strategies that we’re about to talk about in this post to exercise non-predictive control. There’s two complimentary strategies that I know of for how to deal with this Knightian uncertain risk, the first is called Antifragility.



The idea of antifragility comes from Nasim Taleb. The idea is that you’re going to build a system or organization that gains from the disorder inherent in Knightian uncertain risk. If something is fragile. disorder is really bad for it. If something is robust, it can withstand a lot of disorder. If something is antifragile, it is actually strengthened by disorder. For Knightian uncertain risk, antifragility is really the only one of the three that is viable long term. There are only two ways to be antifragile.

Natural Selection

The first type of antifragility is natural selection. In order to take advantage of natural selection, our system or organization must sufficiently diversify its strategies in a real way. We can’t just diversify along one category – we have to truly branch out into other categories and completely different strategies. As long as we have a sufficient numbers of diverse variations, we’ll have a few that survive even the harshest black swans. Whatever strategies are left are the ones that can handle everything that was thrown at us. We then diversify from those strategies to continue to take advantage of natural selection.



The second type of antifragility is Hormesis. Hormesis is creating ideas or strategies that grow from adversity and stress. Our muscles are hermetic, because as we put strain on them they actually grow to handle that strain. Building systems that learn, handle adversity well, and become increasingly stronger due to stress is the second way we can become antifragile.



If you remember back to the congruency episode we talked about masculine and feminine congruency. Masculine congruency is about a system that changes the outside world in order to achieve it’s goals. Feminine congruency is a system that can maintain homeostasis even with external pressure and volatility. Anti-fragility is the femininely congruent response to Knightian uncertain risk. The masculine congruent response is effectuation. Saras Sarasvathy first discovered this process by running through thought experiments with experienced entrepreneurs and picking out patterns.  The basic idea behind effectuation is that rather than coming up with a system that is fine no matter what’s in the bag, we’re going to pour balls into the bag.

The Bird in The Hand Principle

So instead of choosing a goal from all possible goals,  we start out with what you already have. Who do we know? What are our strengths?  What are our resources? Based on that we can come up with a number of goals that can utilize the balls we already have on our person.  This is referred to by Sarasvathy as the Bird in the Hand Principle.

Affordable Loss Principle

From those, we’re only going to choose strategies that minimize the downside risk such that we can afford the loss. This will allow us to keep pouring in balls to the bag until we can finally pull on out again.   This minimization of downside risk is referred to as the affordable loss principle by Sarasvathy.


Patchwork Quilt Principle

Now, once we’ve selected those goals that match with the color balls we have, we’re going to create the market that will allow us to pull the ball out.  We’re going to take those people that you already know and create precommitments and partnerships with them.  This has two effects on our new venture.

First, is it’s going to constrain the possibilities for our organization. The commitment will come with conditions, and those conditions will limit the total number of strategies that the organization can use and the total number of outcomes it can effect.

Second, it’s going to give us more resources and certainty for our organization. We’ll have more money, connections, and strengths that we can leverage to make our organization successful.  We’re constraining what the organization is even as we are broadening what the organization can do.

Then, we start the process again making more and more commitments. Ultimately creating our new market. There’s obvious parallels here to the recursive congruency process we talked about in the congruency episode. This principle of a series of precommitments defining the organization is referred to by Sarasvathy as the Patchwork quilt principle.


In uncertain and volatile times, non-predictive control methods are critical to success.  Every system should be on some continuum from Effectuative to Antifragile, depending on its own values and structure.  Please play around with these processes and use them for yourself.


Anchoring – Mental Model Monday

Anchoring is a way to give yourself new emotional capacities that you’ve never experienced before. Enjoy!

My Learning Algorithm – Mental Model Monday

Learning new things is an incredibly important skill that everyone should have. Here’s my learning algorithm:

The Most Important Things I Learned in 2016

2016 was a big year. It saw some major changes in my life, as I wound down Self-Made Renegade, searched for a new world changing business, settled on Verity, and built an amazing team.

Here are the most important things I learned:

To Build a Great Team, Articulate a Big Vision

My biggest lesson this year was to not be shy about my vision. In the past, I’ve been a bit shy about sharing my ambitions for fear that they may sound crazy. However, I’ve overcome that block this year, and started to be more real with people about what we’re actually building and how it could actually make a dent in the world. T

his led to going from just me searching for a business model at the beginning of 2016, to a team of 7 world class performers this year, all working for sweat equity because they believe in the vision. This is a large shift for me, and I’m going to work on how I can refine my message and ability to inspire in 2017.

Contingency Trumps Consistency

I used to think of habits as these things that they I needed to do and maintain indefinitely in order to see a benefit from them. However, all habits have both cost and reward, and as life circumstances change, I’ve found that some habits don’t make sense for certain life situations. For instance, why do something that motivates me every day when I’m already motivated?

I’ve started to see a large variety of my so called habits as tools in the toolbox. I can put them in there when they don’t make sense, and pull them out when I need them again. For instance, I stopped reviewing my Anki cards for a number of months when I simply didn’t have the time, then caught up on what I’d missed when I had the time again. I restarted filling out my five minute journal as I started to notice a lack of gratitude in my life. And as I tried to restart these habits, I renewed some Beeminder goals in order to make sure the transition to starting these habits would go smoothly. All these habits are things that I may stop again as life circumstances change, and I’m completely okay with that, rather than feeling guilty.

Vlad Zamfir – How to Create a Crypto-Economic Protocol From Scratch

Creating a crypto-economic protocol is hard, as we’ve discovered over the past year of writing the Crystal whitepaper. There are often non-obvious attack vectors, and closing those attack vectors can themselves open up other attack vectors

That’s why I was incredibly interested in Vlad’s talk at Devcon 2 about Correct-By-Construction Casper. Vlad hinted at a rigorous approach to creating provably correct crypto-economic mechanisms, and imagine my surprise when he gave an impromptu talk on crypto-economic mechanism design at the Ethereum Silicon Valley Meetup.

If you’re unaware of what mechanism design is, it’s often referred to as “reverse game theory” – Game theory is about choosing the best moves in a given game, whereas mechanism design is about creating a game, given the moves you desire.

The basic idea that Vlad built on in his talk is that cryptography is the part of your mechanism that ensures the integrity of past moves, and economics is the part of your mechanism that ensures you’ll take the proper future moves.

This can be boiled down into a six step process for creating crypto-economic mechanisms:

  1. Assume an oligopolistic setting. Low coordination costs between people in charge of protocol, high coordination costs between users.

    This removes Schelling-points as an option for a coordination scheme.

  2. Figure out desired behavior of all actors in this setting

    Vlad made a distinction between trying to produce proper outcomes, and trying to produce behavior that produces proper outcomes. He thinks the latter is much easier.

  3. Add economic motivators like rewards, escrows, equity, etc. to every actor that may have misaligned incentives relative to above behaviors.

  4. Create economic rules around above mechanisms that discourage the following behavior:

    • Invalid Protocol Messages: Giving information that is untrue or invalid.

    • Failure to Produce Protocol Messages: Not taking your proper part in the protocol.

    • Omission of Protocol Messages: Failure to let the network know about messages you’ve seen(censorship).

    • Equivocation: Failure to choose a single definition of the truth.

  5. Add cryptography to ensure the integrity of all data from the past.

This process seems basically correct, but also looks easier than it is in practice. Not only do you have to determine economic and cryptographic schemes that can satisfy all these requirements, but you also have to do that while taking into account practical computation limits.

I also wonder how DoSing fits into Vlad’s scheme above. It’s a peculiar form of omission where you can’t tell whom is doing the ommiting.

Casper seems to deal with this right now by just punishing everyone, but that seems to open a class of outside attacks. If I want the protocol to fail I can quickly bring it to it’s knees by just DoSing a few people, thus making everyone lose money until it’s unprofitable for them to secure the network. This type of griefing attack isn’t just theoretical, as we’ve seen with the recent attacks on Dyn and Ethereum.

Vlad made a point in his talk that you need to prove your mechanism under multiple behavior models (other than Nash Equilbrium) but to me that seemed to miss the point. Game Theory is one lens through which to view behavior, and I think formally proving Nash Equilibrium’s is enough to satisfy that you’ve made that lens. Proving another incomplete game theoretic behavior model won’t then somehow show that you’re immune to problems.

Instead, the approach I would recommend is a mental models approach, in which formally proving Nash equilibriums checks one box, but there are other heuristics which can increase or decrease your confidence in the real word correctness of a protocol. A surprisingly great book for these types of heuristics is Building Successful Online Communities: Evidence Based Social Design, by Robert Kraut and Paul Resnik, and I’m of the opinion that it should be on every crypto-economists bookshelf.

Crypto-economics is a new frontier, and opens up the possibility to solve long-standing problems in mechanism design like The Byzantine General’s Problem or Arrows Impossibility Theorom. I’m glad we have people like Vlad pushing the state of the art forward, and hope to see more standardization of crypto-economic methods over the coming years.

Crystal – Crowdsource Everything

Here’s my official presentation from the Ethereum meetup – this presentation also marks the first time we’ll be publicly sharing information about Crystal with the broader community.

How Crowdsourced Decisions Could Change Everything

Could crowdsourced decisions change the fabric of society? In this presentation that I gave at the Ethereum Meetup, I argue that they can. (Watch along with the slides at the bottom of the post).

The Most Important Things I Learned in 2015

2015 saw some major shifts in the way I think and interact with the world. Here’s a list of the top 3 things I learned.

Make Decisions based on Expected Value

Probably the most important concept I learned this year is that of Expected Value.  Everyone knows you have to balance risk and reward.  But how do you do that?  If you’re like I was, you probably think of it in terms of managing the extremes – don’t do things that are REALLY risky, and don’t do things that are BARELY rewarding.

But mathematically, as long as you follow some pretty reasonable rules, there’s actually a different way to think of the relationship between risk and reward – multiply them.  If you have a 10% chance of making $1,000,000 from a venture, and a 90% chance of losing $1,000, the above heuristic would tell you that’s a terrible venture to enter.  But using EV, you get (.1 * $1,000,0000) + (.9 * -$1,000) = $99,1000… which actually seems like quite a nice amount of money.

My fascination with EV started after reading the first linked article above, and ended up with my doing a 1 month experiment where I got into the habit of calculating almost every daily decision based on it’s EV.  While that habit turned out to be too cognitively taxing (factoring in computation cost btw is still an open problem in decision theory), it profoundly changed how I approached the big decisions in my life.

Playing Zero-Sum Games Really Well Doesn’t Raise Their Expected Value

This realization, along with the one above, is what led to me move away from Self-Made Renegade and decide to start focusing on systemic change in startups.  A zero-sum game is a game where in order for a player to win, another player has to lose an equal amount.  Think of Mancala – if you end up getting more beads in your side, the other player has to lose an equal amount of beads.  No matter how many beads you get on your side… it doesn’t change the total amount of beads on the game board.

It turns out that helping liberal arts graduates get their dream job is lot like playing mancala.  No matter how many people I helped get their dream jobs (and I WAS helping lots of people), there was a corresponding group that I wasn’t working with, who I was simply taking jobs away from.  I was getting really good at shuffling happiness around, and having the illusion of making difference, while not really changing anything.  This is what ultimately led me to consider systemic change as the best way to ACTUALLY make a difference.

A Mentor is Just a Friend Who Offers Advice Sometimes

One of the consistent negative patterns in my life has been the ability to develop relationships with mentors.  I consistently distanced myself from mentors because  I felt I somehow wasn’t worthy of their mentorship, or because I didn’t actually feel a connection to them, or because I didn’t feel like I had anything to offer.

All of these reasons really came down having some sort of weird transactional model in my head about the relationship between some more successful than you and yourself.  This year, I began to be able to let go of that model, and just enjoy being around, connecting with, and being silly with people at all levels of personal and professional development.  This is a shift which I’d like to continue to make in 2016.

The Business Case for Dapps – Decentralization as a Strategy

Can Dapps actually be profitable? If your code is free, and your data is free, what left is there to sell?

Vitalik Buterin thinks that there is no killer Dapp. Instead, he sees Dapps as having marginally better qualities among certain dimensions, like uptime and automation – and marginally worse qualities along other dimensions like privacy and speed.

He see's a long tail of apps that will slowly be replaced as people realize that the upsides in those cases marginally outpace the downsides.

Paul Sztorc thinks that Dapps can never be a viable business model. If other Dapps can steal your Dapps code and data, then those Dapps will be like parasites, removing money from the value creators – and disincentivizing new creators from creating on the platform.

Both Paul and Vitalik are deep thinkers, and I think that both of their ideas will come into play with the future adoption of Dapps. However, this exact conversation has played out before, with Open Source Software – and it turns out that OSS proved that there was a third factor that would push adoption of the OSS model – strategy. But before I get into that, I should digress and give a brief history of the OSS debate.

A Brief History of the Open Ideology

The philosophical split between traditional development and the OSS style of development can be traced back to a single, influential essay (and the project it was based on, Linux), called The Cathedral and the Bazaar.

Many consider it to be the bible of the hacker philosophy – small, independent developers working together to solve their own problems can compete with – and ultimately beat – larger companies with less participants who don't care about what they're programming.

If you read through the essay, you'll find the same sentiment that underlies a large portion of the crypto-community today: The sentiment that decentralization has so many benefits that overtaking the centralized establishment is only a matter of time.

Meanwhile, there was another camp saying that OSS would never take off. The argument went that projects need money to survive long term, you can't make money if anyone can fork your code for free (and sell it for cheaper), therefore OSS will lose out in the long run. This is similar to the parasitic model of Dapps that Paul Sztorc has.

Finally, there was a third, more moderate camp. This camp said that OSS had strengths and weaknesses. It would do well in areas where security was paramount, but user experience wasn't, such as protocols. Again, you'll notice that this argument has the same structure as Vitalik's argument.

What The Open Ideology Couldn't Predict

What all three of these ideological groups were doing was looking at what types of software they thought OSS could sustain, then predicting the ecosystem would grow around these applications.

And if you read through these again, you'll notice that none of them predicted the success of Android – an open source OS supported by a for-profit company that gained significant market share against a closed source competitor without completely taking the market.

That's because all of these ideologies were based on the characteristics of open source software, and NOT on the characteristics of the ecosystem that OSS supported. Because of this, none of them predicted the use of OSS as strategic gameplay that could be used depending on the strategic landscape.

Schizophrenic Open

Several astute writers have noted the schizophrenic nature of companies like Google and Facebook to open source. One minute, they'll espouse its merits, the next, they'll lock up their data and their code and sue anybody who uses it in a way they don't want.

And an even more astute group have noticed that these aren't just random instances of opening up their codebase. There's a pattern there.

Open as a Strategy

One of my favorite tech strategists, Simon Wardley, codified these patterns into a series of strategic plays, and realized that only those companies which used open source strategically were actually gaining market share.

What Wardley noticed was that even if the company couldn't make money directly from the software, what they could do with the software was directly affect the gameboard, thus putting them in a situation where they could make money in other ways, such as with their core product.

Android's Open Strategy

Android is a prime example of this. Apple had a closed ecosystem with iOS, and Google knew that the more marketshare iOS smartphones gained, the more likely that Apple could shut Google (as a search component that lived on TOP of that platform) out of that market.

Google bringing in a new, closed system wouldn't get create enough of a coalition to take on an entrenched monopoly.A new closed system is too much of a risk for any partners to take on, and without those partners, taking on a clear market leader becomes nigh impossible (see also: Google+).

The solution to this conundrum is to open source your product, which takes out a lot of the risk for partners of being tied to a losing brand – or even worse, supplementing one market bully for another. Android compatible phones could continue to survive, even if Google's specific initiative died or Google became too restrictive in the market. Open sourcing Android allowed Google to credibly commit against being another bully like iOS.

At the same time, Google, being the leader of that project, could steer the project -using unofficial or official standards – towards a point that was beneficial to them. And indeed, to this day most Android phones use Google as the default search.

This insight – that open allows different market dynamics, is at the heart of why OSS is so prevalent today.

The Open Strategy Demystified

So open allows a coalition of companies who are being bullied by a monopoly to work together on the product that can take that monopoly down.

Wardley identified several other areas where open could succeed based on changing the gameboard. Here's the whole list, all of course based on my own understanding of Wardley's work:

  1. Open can bring a particular value chain function (eg Word Processing) to a more evolved(e.g. commoditized) state, therefore making it cheaper for companies that use that function, or creating a more favorable ecosystem for other forms of strategy.
  2. Open can be a source for talent recruitment
  3. Open can be a credible commitment against bad behavior (for a limited time, until network effects give power back to a project), thus allowing for stronger cooperation in a market.
  4. Once one of the previous strategies are used, open creates holes in the value chain where money can be made, by unbundling services that were previously bundled(e.g. before Ubuntu, the OS and support were bundled).

What I think both Paul and Vitalik are missing in their analyses is that what open did for products, blockchain style decentralization can do for every aspect of the value chain.

Decentralization as a Strategy

I expect Dapps to eventually allow these types of gameplay on every level of the value chain. But before the technology can relevantly effect the lower levels of the value chain, it will start on the level directly below the product itself – the data. The first wave of strategy driven Dapps will be all about open data.

First, I want to acknowledge that the concept of open data isn't something that's unique to Dapps or Ethereum. It's something that people have been trying to do for a while.

But until this point, a single group still had to curate the data and foot the bill for hosting it. Never has their been a git like mechanism for data where it's hosted without cost and guaranteed to be accessible to everyone. Where an API for reading and writing the data is built in to the protocol as a feature.

So what happens when companies can use open data in the same way that they can now use open source?

Data-Monopoly Busting

First, you get a way to take on data monopolies by credibly committing to treat your ecosystem with respect (so they don't fork you with a parasite contract). Google's Knowledge Graph and Facebook's Open Graph are two big examples that will become even more relevant in the coming years. Dapps will create a way for smaller companies who get bullied by these two companies data monopolies to create open alternatives in a way that won't directly make them money, but will save them money in the long run.

Furthermore, big companies will to provide support and direction to these Dapps, in order to drive them towards a favorable state for that company, similarly to what Google did with Android and Chrome.

User-Friendly Business Models

Second, because of the level of transparency and "forkability" (for lack of a better word) at every level of the value chain, Dapps get to a point where they can make credible commitments not only about their ecosystem, but about other consumer oriented concerns such as business model. This means we're going to see a lot of user run/user funded companies that provide similar services as existing companies but provide users with more favorable terms.

Dapp Infrastructure Support

Third, we're going to start seeing even more holes in the value chain that can be filled in by companies. There will definitely be a service gap, just like with open source. And initially I think we'll see a lot of Ubuntu type models where companies provide support for free Dapps.

But what's more interesting to me in terms of value chain holes is the interesting opportunities that user run/user owned companies provide. We're going to see a lot of Dapps that are great products, but need help with infrastructure/regulations/etc and don't have the inhouse expertise to even know that they need those things. I think we're going to be seeing a lot of interesting business models around this idea of Business-Infrastructure-as-a-Commodity.


Fourth, I think we're going to see a lot of companies creating Dapps for recruitment and branding purposes, putting something out there with their name on it that the whole world can use.

Cost Savings

Fifth, we'll see Dapps being used in areas that aren't a companies core business, in order to move lower level value chain functions to a cheaper state for the company to use, or in order to make the gameboard more favorable in other ways (e.g. by driving demand up against a competitors constraint)

Traditional Products

Sixth, I agree with Vitalik, we'll see a long tail of Dapp products that are simply better on the blockchain than off, just like we saw an explosion of low level protocols with open source.

The Risk of Dapps

All that being said, I also agree with Paul. Dapps offer some unprecedented strategic play- but if companies aren't careful, they could end up foolsmating themselves.

What Dapps do is essentially create an ILC environment for the entire ecosystem of Dapps, where the system could take any innovations built on top of smart contracts, leverage that data, and then commoditize the innovation. If companies don't have a strategy for when THEIR section of the value chain gets caught up in that cycle, then ultimately they're playing with fire.

In my next post, I'd like to explore a bit more of the ILC dynamic I see evolving in Dapps, some good strategies companies can use to remain profitable, and counter strategies that Dapp developers interested in breaking large monopolies can use to fight back.

Quadratic Voting Isn’t Just For Voting

Quadratic Voting is a concept created in 2013 by Glen Weyl and Eric Posner.

The basic idea is that you pay for votes based on the square of the amount of votes you’re buying. 1 vote is $1, 2 is $4, 3 is $9, and so on. Some people vote for, some vote against, and just like in normal voting some majority or super-majority wins.

Another important part of quadratic voting is that at the end, the funds are returned to the participants split equally among them. This helps them make up some of their lost utility if they don’t get their way.

What Glen and Eric showed was that as long as everyone follows the rules, QV approaches ideal preference aggregation as more voters participate.

That “as long as everyone follows the rules” part is also important, because one large drawback of QV is that it heavily rewards collusion – and in a decentralized system, Sybil attacks.

But let’s pretend that we had solved the collusion and Sybil attack problems. For what else could you use a preference aggregation algorithm?

Quadratic Utility Functions

When I first read the paper, one idea immediately jumped out at me – if you vote on values, instead of on policies, you can use the amount of for votes minus the amount of against votes to create a weighted linear payoff function. You could then use another system, such as forecasting tournaments or prediction markets to figure out how some policy or organization will effect those different values. Essentially, a Futarchy based system where the “Vote on values” part is handled by QV.

Quadratic Share Pricing

QV has the neat property of where the marginal cost o a vote is always the same as you payed for all previous votes, but the price increases so fast that wealth disparities have to be extreme for wealth to be a decisive factor in the majority.

One place where this seems like a desirable trait is in selling shares of the company (I recognized this this weekend when somebody misinterpreted the above idea as this idea.) You want people to be able to buy into the company more if they believe more in it, but you want to make it really hard for any one shareholder to get a controlling share of the company. If you priced shares quadratically, you get both of these desirable properties.

Random Thoughts on Quadratic Voting

  • Still haven’t seen Eric or Glen address the collusion issue, which is the biggest issue right now with QV.
  • One issue I see with it is that if you have a floor price of a dollar, you’re always guaranteed to get back at least your floor price. This means that voters who care LESS than a dollar are still incentivized to vote the minimum amount. This leads to a strong effect where if there’s people who care only a little on one side of an issue, they’re always incentivized to vote more than they care about, which could overwhelm the people who really care on the other side of the issue (the exact issue that QV is trying to avoid). One solution if you use cryptocurrency is to use infinitely divisible payments and infinitely divisible votes. This way, you have no floor price, and aren’t guaranteed to get back what you put in – you’re then incentivized to vote only what you care about (in practice, all cryptocurrencies have a floor price, but hopefully it’s so low that you care about the issue at least as much as the smallest denomination is worth). Of course, then you’ll have a lot of people putting in very small amounts in hopes of getting large amounts… but this will quickly become unprofitable from an other resources (time/electricity/gas) standpoint as more and more people do it.
  • The third issue is that QV encourages coalitions. I haven’t seen Eric and Glen’s models, but my guess is that they don’t allow for coalitions. If you modeled it out, it’s possible that QV could ultimately end up encouraging a two party system, which is exactly what we want to avoid.
  • The mechanism above shows that QV can allow wealth redistribution towards people who care less, but another interesting aspect of it is that it encourages wealth redistribution over time to people who HAVE less. This is a really cool property of QV, and also something I haven’t seen modeled anywhere. How long would it take for the effects of wealthy people buying extra votes to create total wealth redistribution.

What are the traits of a successful business?

This was a video I dug up from a few years ago, it was a MOOC on Entrepreneurship from Stanford, and I went a bit overboard with the analysis. I think it gives a good overview on some of the theories of what makes a successful business, and this is still some of the stuff I look at to this day when evaluating opportunities.

On… those off days

So, you’re having one of those “off days”. Maybe you didn’t get enough sleep. Maybe somebody said something and it brought up old insecurities. Maybe you just woke up on the wrong side of the bed. Whatever the case, you find yourself slipping back into old emotions and thought patterns, not being able to make eye contact, being clumsy and meek again, instead of the assured confident person you have worked so hard to become.

And you’re killing yourself over it. You hate it. You struggle harder and harder to pull yourself out of the slump. You fix your body language, you try to make some clever comments in the conversation like you normally do. But it all just comes out forced and awkward, plunging you further into your own despair. You try to explain why you’re not your usual self, but you know it’s not really an excuse. The harder you try, the more awkward it becomes, and finally you’re left sitting silently, or going to the bathroom to splash cold water on your face, or going somewhere to get a moment to yourself.

And still, you’re killing yourself. What the fuck is wrong with you? What the fuck happened?

At least, that’s how it is for me. I used to hate days like this, despised myself for becoming so weak again when I knew I could be stronger. And after dozens of these days, at various points in my own growth process, I’ve come to a singular conclusion:

It Doesn’t Matter.

Read it again, in case you missed it the first time.

It Doesn’t Matter.

It’s not a big deal, really. It’s not going to ruin your life, it’s not going to kill you. I used to worry about people seeing this side of me, about how their perceptions of me would change, of what they’d think.

Then I realized something.

Noone Knows.

No one knows that there are insecure thoughts in my head. No one notices every slight mistake I make, every little tremble in my voice. I was putting myself deeper into insecure thoughts for nothing. Very few people even noticed that there was any change.

There were a few tho. A few observant ones, who would see that someone was different about me. That I was a little less confident than I normally was… but nothing happened. The next day, or the next week, when I was back to my normal self, there was no change, no big shift now that they had seen another side of me. Nothing. And I realized my second big epiphany.

Noone Cares.

All these big consequences that I had been dreaming in my head were fantasy. Everybody has an off day sometimes, and it’s not a big deal, taken with all the positive impressions of you they have to the few off days. People notice, then shrug it off.

And finally, there were some people who both noticed and cared. They would worry about me, ask what was wrong, ask if it was something they did. The people who cared that I wasn’t my usual self, cared because they cared about ME. These were the people who were the closest to me, and for these people:

It Doesn’t Matter.

One off day is not going to change their feelings for you. These are the people who will stick with you through thick and thin, one little off day isn’t going to change a thing.

I finally realized that by making these off days such a big deal, all I was doing was giving them power they didn’t deserve. By constantly self monitoring myself, and fighting against myself, I was just making things worse. What started out as a molehill only became a mountain because I piled the dirt myself.

When a day like this strikes me now, I don’t worry about it. I accept it, I understand that things will be a bit different today. Sometimes, if I find myself dwelling on it too much, constantly monitoring myself, I’ll try to get in a little exercise. Do a few jumping jacks, wave my arms, go for a run. Anything to get myself out of my head.

Sometimes, you’ll find that what you thought was an off day was merely an opportunity to express a differet side of your personality. Maybe you’ll be a cool, laid back guy because you have less energy. Maybe you’ll be a crazy wacky guy because your thought patterns all over the place. But by not fighting it, it becomes merely another form of expression.

Other times, you’ll find the weird feeling will go away completely, and you’ll be your normal self in no time.

Still other times, neither of these things will happen. you’ll be that slightly more awkward persona you used to dread so much. But now you know the truth: It Doesn’t Matter. and in realizing this truth, you’ll find the problem won’t be nearly as bad as it had been in the past… a small speedbump, and nothing more.

At the end of the day, instead of beating yourself up, you can look yourself in the mirror and ask yourself the only question that really matters at this point: What can I learn from this?

And the next day, when you wake up refreshed, confident, and alert, you can back to what you do best, knowing with utter certainty that you can handle yourself even at your worst… anything else is easy.

Value Flows

Words are not words… they are just a flowing of value. Not an exchange, but a flow.

You do not judge people based on words, you are just feeling the inherent value that those words express. This comes from the core of the person, everyone can appreciate this inherent value. When you respond (and you respond with all your being, words are just a small part), you are acknowledging this value, and you are giving a part of yourself… letting your value flow forth.

Ever noticed how good music can get you into the moment? Listen to others like you listen to the music. You are merely enjoying the Inherent value that the music is holding in itself. You can start to sing along… Acknowledging the songs value without judging it, as well as giving a part of yourself. I have seen dismal rooms TRANSFORMED when one person comes in just singing a tune. Eventually everybody is doing it… allowing the value to flow throughout the room.

This is the same thing that happens in conversations where somebody is completely in the moment. Their value flows forth, affecting everybody. I have been this person, I have been affected by this person. You know the one I’m talking about.

You too can be this person. There are several ways to get yourself to this state, where you’re not thinking or judging, just letting the value flow throughout the interaction.

The first way to do this is to listen for the inherent value coming from others. Don’t try to listen to their words (hear their words all the same) just try to listen to their value. Passion is the biggest indicator of core value, the easiest to spot. Listen to the passion behind someones words, and you will soon be moved into this state. The Inherent value is always there, passion just brings it to the surface.

The next thing to do is to VOICE YOUR THOUGHTS. Thoughts are poison to the flow of value. Having long trains of thought will disrupt the flow of value. Instead of continuing to have the thoughts voice your thoughts stop thinking. By voicing your thoughts directly after you have them, you are effectively stopping the thought train in its track. Your inherent value is being voiced.

Another important aspect is to feel your value. I don’t know if other people have this, but I actually physically feel this as a WARMTH throughout my body, a stillness in my mind, a fluidity to my movements. Value should flow from you when you are sitting, just sitting. You will get stares, people will feel the value flowing to them.

Senses, use them. Hear, smell, taste, feel, see. Don’t judge. Hear, and be in wonder of the sounds. Smell, and let the joy of the smell spread through you. Taste, let your body be overcome by the sensation. Feel, and let all your warmth be amplified, the touch of a female when you are communicating like this is electric. Value flows from and to you and all becomes amplified. See. See the value. Not physical but tangible still. Value flows.

Feel the flow of value with all your senses.
See the inherent value in all people.
Be the value, until it is YOU who is flowing throughout.

Benjamin Franklin’s Secret to Correcting People Without Seeming Like a Dick

Benjamin Franklin would, when he heard someone mispronounce a word, make note of it without remark. Later, he would use that same word in conversation the correct way, as a way to correct others without invoking defensiveness. You can use a similar tactic to correct misconceptions.

Another Franklin trick to avoid defensiveness is to change the way you advance your argument. Rather than presenting it as infallible truth, present it as your opinion you’re unsure of, as another point of view, or as something you heard.

Thoughts on the Usefulness of Business Plans

Amar Bhide in 1994 wrote an awesome textbook called the Origin and Evolution of new businesses, in which he took the INC 500, removed the outliers, and systematically interviewed and surveyed everyone who was left. One of his findings was that long-form, formal business plans were irrelevant to success.

I was curious, so I did a google scholar search and found this paper which has a good review of the literature. It cites several studies from different lines of research, and they’re all over the map. Some show a positive effect, some show a negative effect, and some show no effect.
This seems to support the null hypothesis. If there was an effect either way, you’d expect more studies to increase certainty one way or the other. The fact that there is no clear trend one way or the other seems to support the idea that any positive or negative effects are just random noise.

I was about 60% sure that business plans had no effect, I would willing to change that certainty to 58% now (it’s possible that there’s a small large effect that we simply can’t control for due to the nature of natural experiments)

However, one idea that did change for me after reading that review is that there may be a class of companies for which business planning is very useful – those companies in industries in which change is very slow. You might even be able to map out on a Wardley Map how useful business planning is to a company. If it’s all the way on the left of the map, long form written planning is less useful, and informal plans are more useful, whereas as it moves towards the right of the map, the reverse becomes true.

Can robots replace tutors?

That sees to be the ambition of Louis van Ahn.  I love love love what Duolingo is doing for evidence based education. That being said, there’s a lot that I have to nitpick at here, but the biggest is the assumption that we should be pitting teachers against technology. Right now, the best teachers I’m sure could CRUSH the best technology when it comes to teaching (the best one-on-one teachers, even more so).   But that won’t be the case in the next 5-10 years.

What we really need to do  figure out where teachers are better than technology, and where they’re worse. Having technology enhance teaching (in a real way, not moving from whiteboards to powerpoint) could really bring about a transformation.

James Altucher’s Advice on Starting a Business

James Altucher was asked in his AMA this question:

Do you have any tips for someone going into the business world? What type of degrees would be nice, what colleges in the country are nice, etc? I heard the business world was tough but very rewarding. Please share how early life was for you when you were starting up.

This was his reply:

Forget everything in your question. Forget these words: “business world” , “degrees”, “colleges”, “tough”.

Here’s what you do: every day try to figure out ten ideas that will create value for people. You need to build the idea muscle or it atrophies like any other muscle. School doesn’t do that. School atrophies your brain.

Don’t expect your ideas to be good. After about six months they will start ot be good. Become an idea machine. help people. Eventually charge people to help them. Now you are in business. Take a fulltime job so you can get paid while you are building your idea muscle.

introduce 2-10 people a day to each other who you think can help each other. Build out your network this way. Deliver value.

That is your schooling. Nothing else will ever work.

To Fix Education, You Need to Start From Scratch

I’m sick of all these “disruptive education startups” that think that the problem with education is distribution. Taking a broken paradigm and moving it to the internet just means that you’ve found a faster and more global way to screw up younger generations. Sure, access to education is an issue, but it’s like replacing the window of a car when the engine is broken, the door won’t open, and you’re driving on 3 wheels.

To truly create a more effective conception of school, you need to question your most basic assumptions:

  • Are children truly helpless learners who need knowledge force fed to them by adults?
  • What’s the ultimate measure of success of a “school”?
  • Are the only methods of funding a school dependent on government and parents?

I don’t think truly transformational education reform will come from within the system. Not from the government, not from private schools, not from charter schools.  Questioning these basic assumptions is too much of a threat to their existence.

Here I want to write a paragraph about how the next generation of school will be so much more effective than traditional schooling that it can’t be ignored. How sending your child a few hours on weekends could be 10X more effective, and how the entrenched education institutions won’t see it coming.  How creating a highly effective, incredibly fun, and totally optional version of “school” could transform the landscape of education almost unrecognizably in a span of 10 years.

But of course, that would be crazy.

Why Gamification Won’t Save Education

I was watching an interview recently between Sal Khan (of Khan Academy) and Elon Musk. In the interview, Elon Musk draws the analogy between his kids being addicted to video games, and getting kids addicted to education.

Suddenly it clicked.  The reason that all these online education companies are putting so much stock in things like badges, points, and challenges.  They think that these things are the reason kids are addicted to video games.

Unfortunately, this is wrong.  Gamification can’t save education, because thinking it can shows fundamental misunderstanding between being able to explain behavior and being able to predict/cause it.

Explaining Behavior Vs. Predicting Behavior

There’s a principle in social psychology which says that an explanation for a behavior can never be reduced to simply evolutionary explanations.  For example, saying “people protect their family because it passes on their genes” can perhaps describe why a particular behavior was selected for. However, it can’t describe:

  1. Why the behavior of protecting a loved one arose in the first place (the very first time).
  2. The particular set of cognitive procsesses, neuorology, and emotions which lead people to protect their family (and therefore it has little predicitive power).

Similarly, when looking at why video games are addicting, you can’t simply point to carrot and stick mechanics, because:

  1. You can’t explain why people bought the video game in the first place.
  2. You can’t explain the particular set of cognitive processes and emotions that caused people to play the game long enough for reinforcement mechanisms to kick in.

Particularly, people who think video games are addicting because of the reinforcement mechanisms are taking a Skinnerian, behaviorist view of human motivation. They’re ignoring the Bandurian, cognitive psychological model that is a big part of what makes humans tick.

What Makes Humans Tick

I don’t think anyone is arguing that humans aren’t driven to maximize pleasure and reward, and minimize pain and effort.  What I am going to argue here is that what gives humans pleasure, particularly in the sphere of learning, is not to get points and badges  (which can only help reinforce what people already want to do) as many gamification experts would have you believe. It’s also not to get money (which only matters up to a basic “enough” threshold) as many behavioral economists would have you believe .

Rather, there are certain innate human drives which give us pleasure in the simple act of fulfilling them.  In the case of video games, I think there are a few clear drivers:

Gamers want to increase their skills in order to:

  1. Create New Things
  2. Solve Problems and Challenges
  3. Satisfy Their Curiosity
  4. Change the Story They Tell About Themselves

This is by no means an exhaustive list, and in fact there are some non-hack theories of gamificationwhich are much more comprehensive.

But those are the four big ones which I personally observe.  To see this in action, let’s take a look at an incredibly addicting yet simple game called cookie clicker.


How Cookie Clicker Taps Into Innate Human Drives

At first glance, this game seems to support the theory that all that you need to do is have some points and badges, and you can get people addicted. After all, the game pretty much involves gaining points and badges by clicking on things, and then using those points to buy things which can make you more points and badges.

However, let’s imagine for a second that this game was called “number clicker”. Instead of getting cookies and building a cookie empire… you simply got “numbers”.  What would you lose from the game mechanics as a hack gamification expert sees them? Nothing.   However, what would you lose from your innate human drives? You’d lose the ability to create something new and vicariouslytell a story about yourself.  Would cookie clicker be as popular and addictive as it is without these factors? No.

What Does This Have To Do With Education?

I think I’ve made the case that merely adding badges and points to education won’t satisfy the innate human drives, and therefore won’t be enough to motivate human behavior.  One way to follow this chain of logic would be to say: Ok, instead of just adding badges and points, let’s make an RPG out of real life.  Let’s correlate real life learning goals to exploring in game worlds, solving in game challenges, and creating an in-game story about ourselves.

But ultimately, this misses the point.

Just look at the kids at the Sudbury School. These kids have no outside gamified structure imposed on them at all.  No grades, no classes, no nothing. And yet, they still manage to do an (while admitedly not optimized) astonishing amount of learning.  In looking at the essays and videos of their students, you can begin to notice trends about why they do what they do:

They’re learning new skills in order to”

  1. Build or create something in the real world.
  2. Solve a problem or challenge in the real world
  3. Satisfy their curiosity about how the world works
  4. Change the story they tell about themselves (I.E, they think it’s cool.)

Again, this isn’t an exhaustive list. But if you’ll notice, this is almost the exact same list that was made above. It turns out real life ALREADY provides the motivation to learn.  So to save education, you merely need to make it easy for kids to connect their learning goals to the real world.

How to Save Education

The ideal motivational platform for students wouldn’t rely on fancy points and badges systems (although it might use those as supplementary reinforcements).  Instead, the perfect motivational platform would do two things really well:

  1. Show you what skills and facts you are able to learn next, based on your current knowledge.
  2. Show you the experiments you could run, things you could create, or real life projects you could participate in, based on those skills and facts.

Unleash a simple learning system like that, in an environment like a Sudbury School, and innate human drives would take care of the rest.

Reinventing education ain’t easy. But it is simple, when you take it down to basic first principles like this.

Anyways, I would love to hear your thoughts on this. Am I simplifying to much? Is there things I’m missing? Let me know through an email, in the comments, or anywhere else.

Why does hiring suck so much?

This is going to be a ton of armchair reasoning coming from someone who doesn’t work in corporate America. But I’ve gone through the hiring process with enough clients who are going into corporate America that I feel like I can say this: Hiring Sucks.

It’s like, the most arbitrary process ever.  We use these outdated things called resumes, to do this ridiculous process called an interview, to reject based on gut feelings and bullshit criteria, using people who aren’t even trained on how to make these ridiculous practices work kinda right – and in the end, we end up with a sub-optimally choosing one of the basic building blocks that makes a company work.

But why does hiring suck so much for the company?  Because hiring isn’t about doing what’s best for the company, it’s about what’s best for strengthening your tribe within the company.   The entire hiring process  subjective resumes, interviewing face to face, etc, all make sense if you realize that the purpose of hiring is to get “allies”, while making it look like you’re getting competent hires.  If you want someone of the proper status with similar viewpoints who will help bolster your viewpoints, it makes sense that you wouldn’t want to take even an obvious step such as blinding a resume, and the more obvious step of removing humans from the process till the very end, and making 90% of the hiring process completely objective.

This is a problem for fixing education – because if you want to change the incentives for education to be optimized for innovation and productivity, the only way to do this is to make the incentives for hiring be optimized for innovation and productivity – as one of the largest incentives for education IS being hired. It makes sense that we have a largely signal based, status based model of higher education right now, because those signals are exactly the type of thing that companies look for when trying to find someone who would make good political allies at work.

To be sure, academia has it’s own entrenched systems and perverse incentives, and government regulation in lower education mucks things up even more – but  that’s all secondary to the fact that if your main goal is to get someone who’s competent enough while fitting into your tribe, you need a system of education that can help fit people into those tribes – and anyone who skirts the system will have a harder timer getting hired.

So if you’re really interested in getting companies to make hires that will cause them to be more productive, more innovative companies  (and not hires that are competent enough, while bolstering the hiring people’s individual political factions), what do you do?    This I think will be hardest part of reworking the whole system of education/hiring/teaching/credentialing.  It’s the one area that I don’t see a clear systemic solution to, but what I do have is the seed of an idea that I hope will grow more as I refine my thinking, talk to people, and experiment.

As I alluded to before, there are a few basic building blocks of an organization:  Their people, their processes, and their business model.  This, combined with the external competition and market forces, determine the success of the organization.

What this means is that, while people aren’t the determining factor of an organization, they’re enough of one that if you could get a sufficient slice of a particular market to take up a hiring method that’s optimized for productivity and innovation, on average those firms would out-compete their competitors in the space.

The problem being of course, is that if you could get a sufficient amount of an industry to uptake this in the first place, I wouldn’t have to make this post.  However, it does mean that there’s some “tipping point” – the point at which the other two factors (business model and processes) are averaged out among all the people who adopt the new rational hiring processes, at which point the new hiring method will come to dominate the industry, because those who don’t adopt it will be out-competed by those who do.

Now, based on some obvious observations,  I’d conjecture that people aren’t willing to hire someone outright incompetent even if they’d bolster their political tribe – hiring incompetent people too much would probably get you fired (ok, I know in the dilbert model of the world, everyone starting at junior management and above is incompetent, but I don’t buy that that’s the result of poor hiring decisions).

So the next question is, how do you find situations where there’s not competent people that will fit in your political tribe? And the obvious answer is in talent constrained components of your business – if competent people are hard to find, then you’ll have to hire them when you can find them, even if they’re not the best political allies.

This is why coding bootcamps and data science bootcamps can get away with skirting the traditional signals of a degree – they create competent (enough) people in an area that’s talent constrained, so the political factor just doesn’t matter as much.

Now, most of those data science and coding bootcamp aren’t in the business of reinventing the hiring process – if they know they’re in the business of reinventing anything, it would be reinventing education.  But that’s a missed opportunity – because if they could somehow slip in some changes to the hiring process, and allow those to propagate through the organization, they’d be able to extend their bootcamp model far beyond talent constrained areas –  they’d be able to extend it to any subject in which a four year degree is stretched to four years because of convention, not because that’s what’s actually needed to learn the subject (this describes a lot of subjects).

The propagating through the organization seems like the hard part – you’d need to prove that this hiring process is better than any other, and do it in a way that doesn’t tip off people that you’re taking away their ability to choose their tribe members.  The nature of talent constrained fields is that on average, you’ll have more incompetent hires using that method, so it would be hard to prove that it’s a better method – and trying to prove this while not letting people realize that their political factions are at stake would be even harder.

But at the very least, it’s reduced the problem – we started with how to change hiring practices globally when that goes against basic human nature, then we figured out that if we could get a subset of a certain industry to change hiring practices, that would likely effect the whole industry, finally we figured out that we’d need to choose industries in which a high percentage of their components where in talent constrained fields – thus being more open to disruptive hiring practices. Where to go from there, I don’t know.  But I do know that you can’t reinvent education without figuring out how to also reinvent hiring.

A strategy should be described on a map on a single sheet of a paper … anything else is wasting my time.
Google is now Alphabet … Happy Hunting. 
Asked “How to deal with inertia to change?” … there’s at least 16 different forms, you have to manage them all
Work in progress: an early attempt at summarizing 11 years of research, experimentation, and teaching… in a confusing one-page diagram. :)
once people start having babies through dating apps, artificial intelligence algorithms will technically be selectively breeding humans
Original three stages of expertise from 2008 – … – NB graphic is based on precisely nothing
Scary good scam mail – email by real person – content credible – link looks like attachment – landingpage looks
… the danger is how much they “ho hum”, dilly dally and then later on react in the “oh no” (i.e. too late)
The backlash to the backlash against p-values 
The #PanamaPapers may be a smoking gun, but really they shouldn’t be a surprise to anyone.