artifical intelligence · Big data · Business Culture · decision making · Investment · Learning · Maths · Statistics

The unrules by Igor Tulchinsky, founder and CEO of WorldQuant

Igor’s rules

  1. The UnRule: all theories and all methods have flaws. Nothing can be proved with absolute certainty is, but anything may be disproved, and nothing that can be articulated can be perfect.
  2. You only live once. Your time on earth is the only truly irreplaceable resource. If today was my last day, what would I be doing with it?
  3. Life is unpredictable. There are limits to planning; the key is to act. Foster opportunities, then take advantage of outcomes. If you have to decide and you can’t, flip a coin. If it’s the wrong action, you will feel it and reverse course. Actions have a compounding effect; it’s bad to deliberate for too long.
  4. Establish concrete quantifiable goals and always go from A to B. Concrete things are attainable. Abstract and nebulous wishes are not.
  5. Develop willpower and persist. The most important limit is how much ability and persistence you have. Age means little.
  6. Play to your strengths, don’t compromise. Weaknesses can only be improved marginally, but strength can be improved more.
  7. Obstacles are information. If you can’t get something to work there is a reason. Learn adjust and attack it again.
  8. Aim for the anxious edge, the point of mild anxiety
  9. Arrogance distorts reality. Arrogance makes you perceive the environment in the way that maximises your ego. Environment does not exist for you, so your perceptions turn into fiction. You make bad decisions by chasing illusions. This gets harder after success when hubris slips in.
  10. Make everyone benefit
  11. Opportunity is unlimited, ideas are infinite
  12. Blame no one else. Minimise regrets.
  13. There is a virtue in economy of expression. Efficiency implies clarity and economy of thought. Pretend you have a fixed number of words in your life. The sooner they are all said, the sooner you’ll die.
  14. Value diverse and competing methods. Because all theories are flawed, the best approach is to collect as many of them as possible and use them all, in as optimal a fashion as you can devise, simultaneously.
  15. Value multiple points of view.
  16. Make everyone benefit. Align your endeavours with everyone around you and you will create your own tail wind.

Quotes and other insights

  1. To be successful in this investment business you have to think about it all the time. Thomas Peterffy
  2. Keep losses small. Profits will take care of themselves. Izzy Englander
  1. Don’t get emotional about your trades. React instantly to bad news. If it’s scary run. Take aggressive risks but manage losses. Aggressive behaviour forces your environment to react to you, rather than the other way around. You’re in control; you have the wider array of options in a higher probability of success. You need an exit route if it doesn’t work out.
  2. In systems with a high degree of interactive complexity, multiple and unexpected interactions of failure are inevitable.
  3. A good business runs itself. And create this by choosing the right people. A lot of time should be invested in that activity. Optimal compensation schemes are vital.
  4. Minimise bureaucracy. Time is money; time is scarce. Bureaucracy wastes time and money. If you have the right people, right systems and the right compensation scheme you can scale without adding bureaucracy.
  5. What makes a good trader? Intelligence, focus, action orientation, and the ability to learn from errors; economy of words and thoughts, honesty, and a strong sense of self; the ability to take risks, compartmentalise, and handle setbacks without ego getting crushed.
  6. What makes a good researcher? Creativity, tenacity, attention to detail, intelligence, relentlessness, follow-through, and top-level programming skills.
  7. What makes a good manager? Empathy, intelligence, creativity, relentlessness, and follow through.
  8. In their view, quantity of alphas is far superior to quality. Quality cannot easily be defined. They seek to maximise exponentially the number of Alphas they pursue.
  9. If data increases exponentially, predictability should improve linearly.
  10. They key to testing ideas is to have good simulation software.
  11. As complexity increases so will the number and frequency of non linear events will also increase (ie many std dev events – rogue waves, schrodinger equation)
  12. Power laws very common in nature. In some systems the largest entity often brakes scale invariance, ie. it is even bigger than predicted eg. In network systems, dominant player much bigger.

WorldQuant online university in financial literacy worth checking out.

Business Culture · Investment · Learning · Psychology

The emotional side of investment decision making with Jason Zweig

Jason Zweig writes The Intelligent Investor column for the Wall Street Journal and is interviewed here by Shane Parish.

Lots of useful stuff, starting for me from about min 26 of the podcast onwards, here are my highlights as well as some of my own complimentary thoughts:

Financial advice

1. One of the biggest distorting forces in financial markets comes through misalignment of incentives (eg. Brokers paid commission encourages turnover). I think this is one of the greatest truths of financial markets. Charlie Munger also points it out as one of his key mental models, never ever underestimate the power of incentives:

I think I’ve been in the top 5% of my age cohort all my life in understanding the power of incentives, and all my life I’ve underestimated it. And never a year passes but I get some surprise that pushes my limit a little farther.

The way you pay your financial advisor, or your investment manager, your staff, your business managers very very strongly dictates whether or not their interests are aligned with yours. He also talks a lot about how to create greater trust between advisors and their clients through better alignment of incentives

An insight from early in my career when I worked on the financial incentive structure for a team of financial service salesmen: these incentive structures are massively powerful but also cannot remain static. Most incentive structures are not perfect. Usually when you implement a new structure to begin with it has the desired effect but after a year or two the participants understand it and start gravitating towards exploiting its weaknesses at which point in time it’s usually a good time to modify it further.

2. It’s very hard in financial markets to tell the difference between good and bad advice. Outcomes are disperse with many driving factors, narratives are only clear in retrospect and easily misappropriated (see earlier post on Narrative Fallacy). Sometimes outcomes can take years to play out and we judge them over shorter time frames. How could you go about judging this: focus more on their process, ask for evidence that that woks in the long term rather than the short term outcomes and watch those incentives very carefully.

3. We tell ourselves lies every day just to live life effectively, to get ourselves out of bed and moving forward. We think we are better than average at almost anything we do otherwise why get out of bed and do anything? We believe in a “Just world” (a psychological paradigm/theory propagated by Melvin Lerner): The underlying belief we have is that most of us are “good” and good things happen to good people, bad things happen to bad people and that we will get what we deserve. If you are a good investor (you do the right things diversifying your portfolio, controlling costs etc) you will get a good outcome. We are devastated when that illusion is stripped away, when bad things happen to good people and we conclude that they must have been a bad person in some way:eg. a bad outcome for a good investor, or a crime committed against a person; we can often be influenced by this set of beliefs to rationalise that the victim/good person must have done something to deserve the outcome. The financial crisis of 2008 stripped away this illusion vey completely where investors followed “good” advisors and lost a lot of money and Jason believes this has broken down a great deal of trust between financial advisors and their clients. (see minute 42 onwards)

Investment decision making

4. Your decision making needs to be evidence based, not intuition based wherever possible. However you also need creativity to see the connections that others do not. These two are in a bit of tension.

5. He gives an absolutely brilliant definition of risk:

Risk is the difference between what investors think they know and what they end up learning about their investments, about financial markets, and about themselves.

But he glosses over discussing this. The reason I think it is so brilliant is it encapsulates three different types of risk we face when we make investment decisions

A. The investment itself turns out to be different from what we expected eg. Earnings disappoint, cashflow disappoints and it goes bankrupt

B. It may be that the investment performs exactly as they expect fundamentally, but financial markets end up pricing it way different from what they expected. Eg. Nominal economic growth is 4 % but bond yields are only 2.5 %, to highlight the thing investors have been most surprised by in the last decade: how low bond yields can go and stay

C. And most importantly, about ourselves. About our emotional reactions to losses, our ability to remain rational during periods of pain. Most of us suffer from tremendous loss aversion, as behavioural economists would call it.

6. The power of not trading. “Both buying and selling are a form of hubris” believing that you know more than other people, or that you have some unique insight into a situation. I am pretty sure this does not apply to every investor as I have seen some very effective investors operate with a very active style but there is definitely a difference between knowing when to act and when you are just reacting to the noise.

7. Minimising risk by simply not being overconfident in your views, a very powerful way of ensuring you don’t blow up, don’t put everything on one bet,

8. To flourish in a bear market you need two things: cash and courage. So going into a bear market you need to make sure you have the cash otherwise there is no chance to have courage. This is not easy, very few institutional investors ever raise cash, they tend to remain fully invested. And there are many situations in bear markets where institutional investors are not given the option to invest because either their clients are panicking or because they have not managed their liquidity and risk appropriately. And in the midst of a bear market it’s very difficult to have courage. Great quote from Benjamin Graham on the subject from the depths of the 1932 crash:

Those with the enterprise lack the money, and those with the money lack the enterprise to by buy stocks cheap

9. Needing to know your own temperament and understand your own emotions is absolutely essential as an investor.

To be a good investor you need independence, scepticism, good judgment and courage. Easier said than done.

In his opinion the best investors are “inversely emotional”. They need to be a little on the autistic spectrum: able to see that others are experiencing severe emotions but able to detach themselves from that emotional gravitational pull and go in the opposite direction. Again interesting examples of Benjamin Graham being described as “Humane but not Human”, Charlie Munger as being simply “rational”.

10. So if you are a regular human being, not on the autistic spectrum, can you teach yourself to be “inversely emotional” like this?

It’s not easy. You have to put policies and procedures in place to help manage the emotions. If you are an alcoholic you don’t walk past the bar on the way home. So avoid stimuli and shut off noise that could distract. Focus on and listen to analysis that’s rational and unemotional. How do you put the right governors in place to manage the emotions during a decision making process? To avoid the temptation to react to short term performance and pain of loss but not to be complacent either? To avoid the enthusiasm of a new idea and seeks the world might be different going forwards from the past even when past patterns are different.

Danny Kahneman says its very difficult to do as an individual but it may be possible to do as an organisation with the right structures in place.

If it is possible to do as an organisation, I suspect it is still very very difficult to do, and most will fail. That is because it takes much more than structure, though structure is a prerequisite. It takes an incredible culture. That’s because the the pressures to conform with a crowd are already operating at a small number of people, it’s hard to be independent and diverse even among a group of colleagues. To not be swayed by the myriad of cognitive biases we each have interacting with each other is a big challenge. Not to allow group think to quickly dominate an idea.

We will have to work very hard at establishing the culture as one that is both creative, but also evidence based and rational rather than driven by emotions which are the natural drivers of many of our actions at a level we ourselves may not even be aware. We need to have good mental hygiene! How to do this practically is, I think this is the topic of a whole separate blogpost!

11. Once again value of history, really understanding the lessons of history. Be a student of financial history!

Here is the link to the podcast:

Listen to Elevate Your Financial IQ from The Knowledge Project with Shane Parrish in Podcasts. https://itunes.apple.com/gb/podcast/the-knowledge-project-with-shane-parrish/id990149481?mt=2&i=1000354857225

Business · Investment · Psychology

Human behavioural motivations and insights with Rory Sutherland

Here is a facinating conversation between Shane Parish of Farnam Street and Rory Sutherland, the Vice Chairman of Ogilvy & Mather Group, one of the largest advertising companies in the world.

Rory started the behavioral insights team at OM and spends his days applying behavioral economics and evolutionary psychology to solve problems for their clients.

The conversation is broad ranging and long (Rory certainly likes the sound of his own voice) but it is littered with hundreds of nuggets of mental models and insights that are applicable to both running a business, investing and understanding trends in the world today including political trends and technological trends like Artificial Intelligence. So well worth a listen.

A few of my takeaways (my own thoughts in italics)

“The problem with (traditional) economics isn’t only that it’s wrong, it’s that it is very creatively limiting, it posits a very one dimensional view of human nature, entirely driven by monetary utility.”

Different consumers respond differently to different distribution channels

Did you know that advertisers have exploited double-blind testing of what works in adverts since the 1800’s, often running different printing presses with different variants of adverts to test which were more successful! The medical world only caught onto this is the mid 1900’s.

In an example he gives an advert in the pre internet era is run in three different forms: an option of responding by mail only, an option responding by telephone only, or an option where both channels were available. In their example the first mail option had a 2.5% response rate, the telephone advert had a 3.5% response rate. The interesting thing is that the combined option had a response rate of around 5.8%, ie. the two different channels for the same thing accessed almost completely different non overlapping groups of consumers. So how you sell something is as important as what you sell.

The value of unpredictability, emotion and irrationality

“It’s impossible for anything completely rational to successfully evolve, because a byproduct of being optimally rational is being completely predictable and if you were completely predictable you would be dead.” In evolutionary terms other aninals would take advantage of you due to your predictability. So there is a danger in looking at everything as an optimisation problem. Our psychology has evolved to be a bit random and unpredictable.

Examples:

People will take advantage of driverless cars because they are programmed to be safe, pedestrians will walk in front of them or mess around with them to have some fun. Driverless cars will not only have to cope with the normal unpredictability of human drivers but with also how humans will evolve their driving because of how they know driverless cars will behave!

Anger is a useful emotion for humans because the potential to provoke it prevents someone from taking advantage you. They know at some point they will get a reaction and that basically keeps most of us well behaved.

A connected though from me is: Will general artificial intelligence have to develop the same evolutionary mechanisms to operate in our world or survive in a world with other GAIs? Like genetic algorithms will it have to have random mutations or like humans have to evolve emotion to create unpredictability to be able to survive and evolve? What are the implications of that?

Some societies have a cultural tilt towards being tolerant of ambiguity, and have more of an attitude to “give the benefit of the doubt” to someone else compared to those who are more rules driven “this is my right so I will assume I can do this and not make for any allowance for others” – the former (Sweden, Ireland, Netherlands) have lower road accident rates than the latter (Germany, the US). People who are less reliant on rules operate more carefully to avoid costly mistakes.

Contextual biases and value signalling

He explores how the value we place on either a good or service is incredibly contextual. For example we will have a bias towards paying more for the same service or good from one provider or another depending on our sense of the brand of the provider. eg we will pay more for a beer from a boutique hotel than for the same beer sold by a pop-up stand even if we take away all of the “other benefits” like the location and atmosphere. In a financial context think of this as a “boutique hedge fund” versus a lowley long only manager with the same alpha! How does this effect how you market your business?

Other insights into human behaviour from Robert Trivers: his work on Reciprocal altruism (another behavioral trait, that we feel obliged to reciprocate towards someone who is altruistic to us) and work on self deception: Self deception is evolutionarily advantageous, as the best way to deceive others is to believe your own deceits.

Our desire for artificial certainty justified by rational models

Beauracrats, business men (and many investors) love a formula because of the artificial sense of certainty that it creates for then when making a decision. That prevents them from having to exercise judgement for which they might be blamed. Avoidance of blame is a key driver in many businesses.

And that creates herding behaviours because of the asymmetry of reward to getting something right (often a small bonus) versus consequence of getting something wrong (getting fired). The “rational boring norm” is the safe place for most businesses and most peopke working for a big business. Most businesses and people are focused on avoiding bad outcomes rather than maximising the opportunity.

This creates bogus rationality where we try to use simple, understandable algorithns to prove that the decision was logical rather than take a risk of complex judgement demanded in understanding a more complex system: like the financial markets. Does this have potential consequences for the current trend towards “smart beta” algorithms in investing?

This leads on to the role of imagination vs reasoning in decision making. It usually takes takes imagination to formulate an insight or hypothesis and then logic to prove whether it is true. The role of imagination is often downplayed because afterwards we present the thinking as if the linear sequence of the logic led us to the conclusion. Science and insights are very seldom arrived the linear way.

He highlights three different forms of reasoning

Forward reasoning: what will happen next because I know of what has happened so far, based on our heuristics of how the world works. We then can test out model or heuristic by seeing if the outcome matched our expectation. If not we need imagination to hypothesise how to change our models or heuristics.

Reverse reasoning: like a detective, I observe an outcome and then I hypothesise what preconditions could lead to it and like a detective I sift through possibilities. This requires a lot of imagination to hypothesise what could lead to the outcome.

Post-rational reasoning: what we usually do when we come to a conclusion: our emotional limbic system reacts very quickly resulting in a conclusion or action using its heuristic systems in a situation, afterwards we justify the outcome by coming up with a narrative or logic that justifies the decision (fitting the facts to what we want the answer to be). We are post-rational creatures rather than rational creatures. Most of our reasoning happens after we feel an emotion, to justify why we feel the emotion.

The psychology of choices and value.

On a menu we can use the price of different items to signal worth or quality. The menu context pushes people towards selecting certain options, for example seldom choosing the most expensive or cheapest option on the menu, or the more expensive options being indicative of higher quality.

Game theory and the difference between one-off games vs repeat games.

In a repeat game you don’t just optimise your outcome for the next move but for many moves into the future. For example in selling to someone, you don’t just want this sale but future sales to the same customer, so you had better make sure that this experience is a good one for the customer even if that does not maximise your short term profit. In long term games you have to be tustworthy and reputation is incredibly important. Consumers make many choices based on their intuitive understanding of service providers trustworthiness and alignment. The bigger the upfront cost of committing to a decision that takes a long time to see results the more the long term alignment will matter.

There are many practical applications in every day business. For example the focus by many large businesses incentivised by stock market behaviour towards short term earnings results is a bias toward “one time games” to make themselves rich. Think of the contrast between hedge funds with annual performance fees, private equity with 5 or 7 year incentivisation and Amazon with a very long horizon strategy for growing the business through reinvestment.

The value of experimentation.

In business you don’t have to be right all the time, you can be experimental and see what works and what doesnt (provided the cost to testing is low). It is important to test some counterintuitive or imaginative ideas because as its much more valuable when they pay off as your competitors won’t or are unlikely to have tested them.

In the case of the financial markets the payoff of a contrarian bet may be large as it is counter to expectations with little downside if the crowd is already pricing in the consensus scenario.

So have a listen here

https://www.farnamstreetblog.com/2017/06/rory-sutherland-podcast/

Investment · Learning · Psychology · Statistics

Super Forecasting

[Farnam Street Podcast] Philip Tetlock on The Art and Science of Super Forecasting

http://podplayer.net/#/?id=24093071 via @PodcastAddict

Phil Tetlock forms teams of “super forecasters” who are amazing accurate at predicting the probabilites of real world events, in their case often used to predict the likelihoods of complex real world events for the intelligence community.

It has many insights useful to invesmtent management and I am sure other fields that depend on probabilities.

1. Start with the outsiders view. Establish baseline probabilities of how likely something is before you start refining with inside knowledge. Eg in predicting how likely someone specific is to get divorced start with the probabilities of anyone getting divorced.

2. Break the problem down into steps in a decision tree each with their own probability. You can then work on refining each node in the tree. That way you know what the key questions are that you are asking.

3. Focus on accurate statement of the prediction, many of us are managing career risk for fear of being wrong, creating fuzzy statements that could be right under a wide range of outcomes.

4. Being open minded is essential to being a good forecaster. We all like to think we are open minded – we really are usually not. We can be more open minded on things we are not ideological about but where we have ideologies its much more difficult to be open minded

5. A group of people with the same objective and a good debating style but where possible with very independent thought processes can operate far more effectively to get to the right probababilities.

6. There are lots of impediments to making accurate forecasts in organisations where the objective of accuracy may be to further your career, not rock the political boat or not be seen to make a mistake rather than getting to the right answer. Manging that culturally is a challenge that leadership have to undertake: ensuring that the goal is the accuracy, that open mindedness is real and that mistakes are welcomed to learn from.

7. One of the biggest risks is conflating mistakes with probabilistic outcomes. You thought the probabilty of an outcome was 75%, the alternative outcome actually happened. That does not mean your probability was wrong, it could just have been the 1 in 4 chance of the other outcome happening.

Investment · Learning · Psychology

Ask “why?” five times…. Proximate causes vs root causes, Narrative fallacy and Mental Models

Great post for people in the investment field:

Farnham Street is an amazing blog dedicated to discussing mental models and they do a great job of explaining the challenges of proximate vs root causes and how to deal with the deductive problems that can occur when we jump to conclusions too quickly.

https://www.farnamstreetblog.com/2017/05/proximate-vs-root-causes/

In investments we are always coming up with narrative to explain what happened. The financial press exist purely on this, reporting the day after on why the markets have moved the way they have as if it’s an obvious truth. Making an investment is much harder, you need to look forward to what is likely to happen.

When assessing our investment outcomes we are also immensely susceptible to a whole range of behavioural biases. The issue of proximate cause and narrative fallacy often comes up.

Proximate cause: The manager lost a lot of money because the position was very big and the stock blew up because company management were hiding what they were really doing and no one could have know. Root causes: they failed to size the position correctly because they failed to assess the risk in the investment correctly, they failed to assess the risk because they were trying to cover too many different investments without enough resource, or they failed to assess the risk properly because they did not have enough independent challenge to the key decision maker, or they failed to assess the risk properly because the analyst was not diligent enough/experienced enough to identify the risks…

Each of those different causes has very different corrective actions and very different reactions that we should have as asset allocators.

The opposite can also be true: the manager lost money, what an idiot, obviously they should have seen the risks, because now with hindsight we have the narrative stating how obvious it was. But at the time they understood the risks. They sized appropriately for the risks. And things just didn’t go their way this time, which will happen 45% of the time for most money managers.

The key to differentiating between these different potential cases is asking the right questions, and asking more and more questions. Digging deeper and challenging your assumptions.

In the Farnham Street article the particular route I like is asking “Why?” 5 times?

Why did you lose money? We sized it to big and got it wrong.

Why did you size it to big? We misestimated the risk.

Why did you misestaimate the risk? The analyst was an idiot.

Why was the analyst an idiot? Oh they are not really an idiot. It’s because we left him to do the work on their own and did not provide independent challenge

Why did you not provide independent challenge? Because I was too distracted on something else.

It reminds me of Ricardo Semler, who applies this approach to much of his business decisions, for him it seems to work when you just ask Why three times?

He is interviewed by Tim Ferriss episode 229 which can be found here. It’s a really inspirational podcast that covers his approach to building a phenomenally successful business with a very unconventional management style, well worth a listen for all sorts of good reasons.

https://itunes.apple.com/gb/podcast/the-tim-ferriss-show/id863897795?mt=2

The Farnham Street post also has a great explanation of other mental models which are all relevant to Investment decision making if you read down to the end.

Business Culture · Investment · Learning · Psychology

Improving decision making in committees

A review of decision making literature from UCL on what makes for good decisions in committees is particularly relevant for investment committees:

1. Diversity of participants (functional diversity) with different independent expertise or knowledge makes optimal decision making more likely (finding global rather than local maxima more likely with different starting points for the mathematical optimisation geeks)

2. Slower decision making is more accurate. (And conversely sometimes you need to trade speed for accuracy).

3. Don’t vote, prefer discussion. Getting to a consensus answer or leave it to an expert final choice, if you do vote it needs to expertise weighted (ie some votes count more than others) based on objective expertise measurement, not one man one vote.

4. Over confidence bias: The worse you are at something the more delusional you are that you know the right answer! More expertise often results in less certainty from expert individuals: the problem is they know how much they don’t know and they can take other less expert people’s opinion into account too much. Go with the experts opinion after having the discussion and as the expert balance your opinion carefully with the input from others.

Podcast: The Naked Scientist

Episode 22/08/15

Dan Bang University College London 5:40 mins up to 10:40 mins

https://itunes.apple.com/gb/podcast/the-naked-scientists-podcast/id74171648?mt=2&i=1000391340611

Art · Investment · Photography · Psychology

Creative Constraints

I was listening to a podcast which reminded me of a really interesting counterintuitive truth: we are more creative when we have constraints.

People think creativity will happen when “I make enough space in my life and have this perfect zen moment and can unleash my creativity.” But creativity seems to come better when we place constraints around it.

What sort of constraints?
1. Completely arbitrary random constraints: it seems these are the most powerful way to unleash pure creativity*
e.g.1. For a photographer: Today I can only photograph pictures with red in them. Suddenly you start noticing things all over the show that have red in them. Suddenly you start looking much more carefully, it becomes a fun objective.
E.g.2. For a writer: Describe the forest only with words beginning with T and O… give it a try and see how much more interesting your descriptions have to be with this sort of constraint in place
2. Structure constraints: e.g. This project has to consist of exactly six photographs. The pictures have to be circular. Once you define a structure you start thinking about what will fit that structure and again looking for something different.
3. Spacial constraints: e.g.1. Take 200 unique photographs of this small space eg. Your kitchen. The first 50’are unique and cliche, the next 50 you start running out of normal ideas and after 100 the magic starts, you start seeing differently. E.g.2. I am only going to write while on the top desk of a bus travelling around London…
3. Time constraints: I have got to get this project done by July. By next week Friday. By Tomorrow. When that deadline is immovable we all come up with the goods…

So give it a try next time you are trying to do something a little more creatively…. constrain yourself!

* the idea of random arbitrary constraints reminds me a little of the approach Derren Brown recommends for memorising something using absurd imagery: if you create an absurd mental image of something you are trying to remember, you remember it much more easily. For example I was once trying to remember the names of a family I had met recently (as I am always forgetting names), so I pictured them as a giant dad of a man in a caveman’s loincloth (it had an association with his name. It I won’t give it away here!), standing on a map of the world on Sweden (that’s whether they were from) surrounded by his family in various other absurd poses and dress. To this day I can still rebuild the entire mental picture and I remember their names and where they are from. The more absurd we create a mental image the better our brain remembers it. I wonder whether it’s a similar thing going on with the creative constraints. It’s forcing our brain out of the everyday “auto pilot” and into a place where it has to do some work which then unleashes the creativity.

Here is the podcast, from The Psych Files by Michael Britt episode 269: How to get people to be creative
http://www.thepsychfiles.com/2016/11/ep-269-how-to-get-people-to-be-creative/