Variety of Decision Making

Over the past several years, an anthropologist (Thompson), a control engineer (Beck) and an actuary (Ingram) have formed an unlikely collaboration that has resulted in countless discussions among the three of us along with several published (and posted) documents.

Our work was first planned in 2018. One further part of what was planned is still under development — the application of these ideas to economic thinking. This is previewed in document (2) below, where it is presented as Institutional Evolutionary Economics.

Here are abstracts and links to the existing documents:

1. Model Governance and Rational Adaptability in Enterprise Risk Management, January 2020, AFIR-ERM section of the International Actuarial Association. The problem context here is what has been called the “Insurance Cycle”. In this cycle we recognize four qualitatively different risk environments, or seasons of risk. We address the use of models for supporting an insurer’s decision making for enterprise risk management (ERM) across all four seasons of the cycle. In particular, the report focuses expressly on: first, the matter of governance for dealing with model risk; and, second, model support for Rational Adaptability (RA) at the transitions among the seasons of risk. This latter examines what may happen around the turning points in the insurance cycle (any cycle, for that matter), when the risk of a model generating flawed foresight will generally be at its highest.
2. Modeling the Variety of Decision Making, August 2021, Joint Risk Management Section. The four qualitatively different seasons of risk call for four distinctly different risk-coping decision rules. And if exercising those strategies is to be supported and informed by a model, four qualitatively different parameterizations of the model are also required. This is the variety of decision making that is being modeled. Except that we propose and develop in this work a first blueprint for a fifth decision-making strategy, to which we refer as the adaptor. It is a strategy for assisting the process of RA in ERM and navigating adaptively through all the seasons of risk, insurance cycle after insurance cycle. What is more, the variety of everyday risk-coping decision rules and supporting models can be substituted by a single corresponding rule and model whose parameters vary (slowly) with time, as the model tracks the seasonal business and risk transitions.
3. The Adaptor Emerges, December 2021, The Actuary Magazine, Society of Actuaries. The adaptor strategy focuses on strategic change: on the chops and changes among the seasons of risk over the longer term. The attention of actuaries coping with everyday risk is necessarily focused on the short term. When the facts change qualitatively, as indeed they did during the pandemic, mindsets, models, and customary everyday rules must be changed. Our adaptor indeed emerged during the pandemic, albeit coincidentally, since such was already implied in RA for ERM.
4. An Adaptor Strategy for Enterprise Risk Management, April 2022, Risk Management Newsletter, Joint Risk Management Section. In our earlier work (2009-13), something called the “Surprise Game” was introduced and experimented with. In it, simulated businesses are obliged to be surprised and shaken into eventually switching their risk-coping decision strategies as the seasons of risk undergo qualitative seasonal shifts and transitions. That “eventually” can be much delayed, with poor business performance accumulating all the while. In control engineering, the logic of the Surprise Game is closely similar to something called cascade control. We show how the adaptor strategy is akin to switching the “autopilot” in the company driving seat of risk-coping, but ideally much more promptly than waiting (and waiting) for any eventual surprise to dawn on the occupant of the driving seat.
5. An Adaptor Strategy for Enterprise Risk Management (Part 2), July 2022, Risk Management Newsletter, Joint Risk Management Section. Rather than its switching function, the priority of the adaptor strategy should really be that of nurturing the human and financial resources in the makeup of a business — so that the business can perform with resilience, season in, season out, economic cycle after economic cycle. The nurturing function can be informed and supported by an adaptor “dashboard”. For example, the dashboard can be designed to alert the adaptor to the impending loss or surfeit of personnel skilled in implementing any one of the four risk-coping strategies of RA for ERM. We cite evidence of such a dashboard from both the insurance industry and an innovation ecosystem in Linz, Austria.
6. Adaptor Exceptionalism:Structural Change & Systems Thinking, March 2022, RISKVIEWS, Here we link Parts 1 and 2 of the Risk Management Newsletter article ((4) and (5) above). When we talk of “when the facts change, we change our mindsets”, we are essentially talking about structural change in a system, most familiarly, the economy. One way of grasping the essence of this, hence the essence of the invaluable (but elusive) systemic property of resilience, is through the control engineering device of a much simplified model of the system with a parameterization that changes relatively slowly over time — the adaptor model of document (2) above, in fact. This work begins to show how the nurturing function of the adaptor strategy is so important for the achievement of resilient business performance.
7. Adaptor Strategy: Foresight, May 2022, RISKVIEWS. This is a postscript to the two-part Newsletter article and, indeed, its linking technical support material of document (6). It identifies a third possible component of an adaptor strategy: that of deliberately probing the uncertainties in business behaviour and its surrounding risk environment. This probing function derives directly from the principle of “dual adaptive control” — something associated with systems such as guided missiles. Heaven forbid: that such should be the outcome of a discussion between the control engineer, the actuary, and the anthropologist!

Still to be completed is the full exposition of Institutional Evolutionary Economics that is previewed in Section 1 of Modeling the Variety of Decision Making (Item 2 above).

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A Fatal Flaw in Reasoning

Financial economics has a basic fundamental fatal flaw. That flaw is that:

Financial Economics assumes that no one pays any attention to Financial Economics.

So if we stopped reading and listening and thinking about the insights of financial economics, they are at least somewhat more likely to actually be true.  At least for longer than they are now. 

But in the recent past, say the last 40 years, more and more people are trying to use the insights of financial economics to guide their actions in the financial markets. 

The problem is that once they do that, they are no longer acting like the rational actors that financial economics assumes that they are.  Those rational actors would have used their own insights about the way that markets work to make their decisions.  That is the way that decisions were made during the past time periods that financial economists studied to prove that their theories were reasonable.  Perhaps many people in those historical periods were being informed by earlier, now discredited, financial theories. 

But now, most people who now move money in the financial markets are informed by the exact same financial theories.  That is different from the past because now we have better communications and better education systems so that these best ideas are much more ubiquitous.  So there are large groups of folks who are all using the exact methods of analysis and decision making.  Those methods often are based upon a freeze tag assumption. 

Freeze tag is the children’s game where one person is it and as he or she tags the other palyers, they all freeze. 

The Freeze tag assumption that is built into the models that everyone uses is the assumption that we are all marginal to the market.  We are assuming that while we are doing our analysis and making our choise that the entire market is frozen AND that no one else is doing the analysis or making the decisions that we are making. 

“The technical explanation is that the market-sensitive risk models used by thousands of market participants work on the assumption that each user is the only person using them.”  Avinash Persaud

Financial economics is like magic that gets less and less powerful as more and more people learn it.  Once everyone can pull a coin from behind an ear, no one is very impressed by that trick. 

So the very success and power of financial economics ability to explain and predict financial markets resulted in more and more people adopting it which led to more and more herding of financial behaviors. 

That has led to a secondary issue.  Smart traders know this.  So just as financial economics provided the point of view and formulas and tools to look at how markets should act that applied to the world without financial economics, the smartest traders are looking at the world with financial economics to make their choices of trades to make their profits.  That takes the economic markets not one but two or more steps away from the pre financial economics markets. 

Game theorists have a game that they like where a group of folks are asked to guess the value of 2/3 of the average of their guesses.  If the range of possible guesses is 1 to 100, then all guesses above 66 are impossible, so the “right” answer is 44. But if only numbers below 66 are rational guesses, then you can rationally eliminate guesses above 2/3 of that value and so on until the only logical guess is 0. 

It is the problem that Keynes talked about with beauty contests (and stock markets):

“It is not a case of choosing those [faces] that, to the best of one’s judgment, are really the prettiest, nor even those that average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practice the fourth, fifth and higher degrees.” (Keynes, General Theory of Employment Interest and Money, 1936).

Every financial economics theory has a tail like that.  Think about risk free rates.  Once you tell someone that a particluar interest rate is risk free, then those who can borrow at that rate will borrow more and more and more until the borrower is no longer  riskless.  An observation that a praticular rate was a good proxy for a risk free rate can only be correct in retrospect. In any forward sense, it is highly likely to be untrue. 

Businesses are hedging based upon measures of sensitivity of certain instruments to underlying financial information, “the greeks”.  But in October 1987, we found that those sensitivities were, in fact, totally variable based upon the number of people who were relying upon those relationships. 

This is all true because of the flaw in financial economics.  It would never have happened if financial economics papers were not published but were instead only read aloud at economics conferences. 

So what can risk managers learn from this story?  (This blog is for discussing matters of interest to risk managers, so that question is applied to each and every post.)

Risk managers can learn two things:  First, we need to have models that are not  based upon freeze tag type assumptions where “no one but us” knows of the theory.  And second, we need to be careful to try to not ourselves fall into this sort of cycle by getting into a process of trying to guess what everyone else’s risk models will be telling them.

An ERM Carol

You awake with a start.  There is an eerie presence in your bedroom.  A voice says “Come with me!”

You see yourself, many years ago, starting out in your career.  With an interest in risk, you feel lucky that you were able to land a position in an insurance company.  You are encouraged when you hear your boss say “its all about risk and reward”.  But it didn’t take you too long to find out that while there were daily, weekly, monthly, quarterly, annual and special reports about the rewards that the company was experiencing, there was not one single report about risk.  You confront your manager about this and he tells you that “risk isn’t something that you measure”, it is in your gut.  You just know when something is risky. “.  He advised that once you were more experienced, you too would be able to tell when something was risky or not.  

You drift back to sleep when a second voice calls you to “Behold!”.  You see yourself a manager in an insurance company:

You are being told that risk is very important. Your company takes risk management very seriously. Several years ago, the company spent millions to build a state of the art Economic Capital Model.  Now, all plans and all performance is viewed in terms of the amount of risk associated with each and every activity.  And you hate the whole thing!

To you, this has become a technocratic nightmare.  Your performance is judged by a computer using an algorithm that seems to be spewing forth somewhat random values.  It seems like your promotions and bonuses are being determined by a slot machine, but a slot machine with no window to see what is happening inside.

The high priests of risk operate the model.  But they are too busy to actually explain what is going on in a manner that could help the business.

So if somehow, you are lucky enough to get to the top, that will be the last day for that complex risk model.

And you pull the covers up over your head.  This is too much like a workday.  You need your sleep.  But before long, a third voice wakes you again.   “This way…”

You are on the hot seat.  The board wants to know how the company was able to get into such a problem.  Didn’t you see that there were such enormous build ups of exposures to that risky indoor snow experience sector?  The frostbite claims were double what they were last year.  Dividends will have to be eliminated.  And we probably need to turn down the corporate air conditioners.  No longer could the offices be kept at a tolerable 31 degrees.  Next summer would be unbearable.  Your only defense is that your gut told you that there was little risk and big rewards in the indoor snow business.  But that is not how it went.  They end the meeting by letting you go.  The inglorious end to your career as a risk manager. 

You wake up shouting that it was not your fault.  And you see the light coming in the window.  You turn on the TV to find that all this happened in one night.  You get dressed and go back into the office.  You are finishing up your staff meeting and you direct your attention to your risk management staff.

Starting today, I want you to spend more of your time making your models more transparant and the findings more actionable.  I am tired of risk being something that comes at us after the fact to tell us that something was wrong.  We need to focus on leading indicators that all of the managers can use in real time to manage the business.  You can still use that fancy model that you all so love, but I only want to hear about the model when it actually explains something about the business that I can use next quarter to do a better job of managing my risk and reward.

And with that, we ended the meeting and all went to our holiday party.  Next year will be interesting…..

Self Perpetuating Pessimism – Just the Opposite in Insurance

Things have been bad.  The results have run against your strategy for some time now.  So you pull back.  Derisk.  Leave the game.

That may be the most prudent decision, or it may be simply self perpetuating pessimism.

Others may not share that pessimism.  Others may see opportunities in the chaos that has caused your losses.  Others are doubling down.

But how to know?  How to tell whether it makes more sense to move on or to stay in?

Sometimes, you cannot tell.  There is no indications whether the next day or the next year will be even worse than the last or whether it will be a big step on the road back to prosperity.  The most important element to determining that may not be something neutral in the environment.  It may be the mood of the people.

“The only thing to fear is fear itself”

The actions of the crowd to pull back all together cause or at least accentuate the very poor environment that the actions were meant to protect against.  It is a classical negative feedback look.  The poor results cause people to pull back that causes more poor results.

That is the way that an investment market works.  But an insurance market works just the opposite.  Optimism makes more people to rush into a market.  It causes the rates charged for a risk to go down because of competition.  It causes underwriting standards to deteriorate.  It encourages more and more of the underpriced misunderwritten business.  Pessimism causes insurers to withdraw from an insurance market.  Less competition allows rates to rise.  Pessimism makes insurers set the minimum rate at which they might write some insurance higher.

In insurance when the crowd deserts a market, the few who remain are suddenly able to raise rates to sustainable levels and beyond.

So why does it take so long for insurers to react to soft rates and leave markets?   They all know the game and all want to be the ones who are left when the rates get hard. So insurers are playing a few moves ahead.

For some reason, investors, who consider themselves to be much more sophisticated, only seem to be looking one step ahead.  Or at least the crowd does.  Maybe the real sophisticated investors ARE playing several steps ahead.  They will never let you know that.

Cascading Failures

Most of the risks that concern us exist in systems. In massively complex systems.

However, our approach to risk assessment is often to isolate certain risk/loss events and treat them totally marginally.  That works fine when the events are actually marginal to the system but it may put us in a worse situation if the event triggers a cascading failure.

Within a system cycles are found.  Cycles that can ebb and flow over a long time.  And cycles that are self dampening or cycles that are self reinforcing.

The classic epidemiological disease model is an example of a self dampening system.  The dampening is caused by the fact that disease spread is self limiting.  Any person will have so many contacts with other people that might be sufficient to spread a disease were they infected.  In most disease situations, the spread of the disease starts to wane when enough people have already been exposed to the disease and developed immunity so that a significant fraction of the contacts that a newly infected and contagious person might have are already immune.  This produces the “S” curve of a disease. See  The Dynamics of SARS: Plotting the Risk of Epidemic Disasters.

The behavior of a financial markets in a large loss situation is a self reinforcing cycle.  Losses cause institutional investors to lose capital and because of their gearing the loss of capital triggers the need to reduce exposures which means selling into a falling market resulting in more losses.  Often the only cure is to close the market and hope that some exogenous event changes something.

These cascading situations are why the “tail” events are so terribly large compared to the ordinary events.

Each system has its own tipping point.  Your risk appetite should reflect how much you know about the tipping point of the system that each of your risks exists in.

And if you do not know the tipping point…

Major Regime Change – The Debt Crisis

A regime change is a corner that you cannot see around until you get to it.  It is when many of the old assumptions no longer hold.  It is the start of a new set of patterns.  Regime changes are not necessarily bad, but they are disruptive.  Many of the things that made people and companies successful under the old regime will no longer work.  But there will be completely new things that will now work.

The current regime has lasted for over 50 years.  Over that time, debt went all in one direction – UP.  Most other financial variables went up and down over that time, but their variability was in the context of a money supply that was generally growing somewhat faster than the economy.

Increasing debt funds some of the growth that has fueled the world economies over that time.

But that was a ride that could not go on forever.  At some point in time the debt servicing gets to be too high in comparison to the capacity of the economy.  The economy has gone through the stage of hedge lending (see Financial Instability) where activities are able to afford payments on their debt as well as repayment of principal long ago.  The economy is in the stage of Speculative Finance where activities are able to afford payments on the debt, but not the repayment of principal.  The efforts to pay down debt will tell us whether it is possible to reverse course on that.  If one looks ahead to the massive pensions crisis that looms in the moderate term, then you would likely judge that the economy is in Ponzi Financing land where the economy can neither afford the debt servicing or the payment of principal.

All this seems to be pointing towards a regime change regarding the level of debt and other forward obligations in society.  With that regime change, the world economy may shift to a regime of long term contraction in the amount of debt or else a sudden contraction (default) followed by a long period of massive caution and reduced lending.

Riskviews does not have a prediction for when this will happen or what other things will change when that regime change takes place.  But risk managers are urged to take into account that any models that are calibrated to historical experience may well mislead the users.  And market consistent models may also mislead for long term decision making (or is that will continue to mislead for long term decision making – how else to characterize a spot calculation) until the markets come to incorporate the impact of a regime change.

This may be felt in terms of further extension of the uncertainty that has dogged some markets since the financial crisis or in some other manner.

However it materializes, we will be living in interesting times.

Modeling Uncertainty

The message that windows gives when you are copying a large number of files gives a good example of an uncertain environment.  That process recently took over 30 minutes and over the course of that time, the message box was constantly flashing completely different information about the time remaining.  Over the course of one minute in the middle of that process the readings were:

8 minutes remaining

53 minutes remaining

45 minutes remaining

3 minutes remaining

11 minutes remaining

It is not true that the answer is random.  But with the process that Microsoft has chosen to apply to the problem, the answer is certainly unknowable.  For an expected value to vary over a very short period of time by such a range – that is what I would think that a model reflecting uncertainty would  look like.

An uncertain situation could be one where you cannot tell the mean or the standard deviation because there does not seem to be any repeatable pattern to the experience.

Those uncertain times are when the regular model – the one with the steady mean and variance – does not seem to give any useful information.

The flip side of the uncertain times and the model with unsteady mean and variance that represents those times is the person who expects that things will be unpredictable.  That person will be surprised if there is an extended period of time when experience follows a stable pattern, either good or bad or even a stable pattern centered around zero with gains and losses.  In any of those situations, the competitors of that uncertain expecting person will be able to use their models to run their businesses and to reap profits from things that their models tell them about the world and their risks.

The uncertainty expecting person is not likely to trust a model to give them any advice about the world.  Their model would not have cycles of predictable length.  They would not expect the world to even conform to a model with the volatile mean and variance of their expectation, because they expect that they would probably get the volatility of the mean and variance wrong.

That is just the way that they expect it will happen.   A new Black Swan every morning.

Correction, not every morning, that would be regular.  Some mornings.

Momentum Risk

How many times have you heard this

If it isn’t broken don’t fix it.

As a risk manager, momentum risk is one of the most difficult risk to overcome.  (I wonder how many times on these posts I have claimed this?)

But this is the aspect of the Horizon disaster that led to millions and millions of barrels of oil spilling into the Gulf.  Before that the oil companies claimed that there had never been a failure of an oil rig in the Gulf.  So that was the Momentum assumption.  It had never failed so it never would fail.

Standing against that is the seemly endlessly negative point of view of the risk manager:

If anything can go wrong, it will.

Murphy‘s Law is usually taken as the ultimate statement of negative pessimism.  But instead you the risk manager need to use Murphy’s law as he did.  As a mantra to keep repeating to yourself as you look for ways to stress test a system.

Looking to engineering (Murphy was an engineer you know) for some thinking about stress to failure, we find this post:

When a component is subject to increasing loads it eventually fails.   It is comparatively easy to determine the point of failure of a component subject to a single tensile force. The strength data on the material identifies this strength.   However when the material is subject to a number of loads in different directions some of which are tensile and some of which are shear, then the determination of the point of failure is more complicated…

Some of your stress to failure tests will have to be tensile, some compressive, some shear, in different directions and in different combinations.  You should do this sort of testing to know the weakest points of your system.

But there is no guarantee that the system will fail at the weakest points either.  In fact, you may put in place methods to reduce stresses to those weakest points.  Remember that now elevates other points to be the new stresses.

And do not let Momentum thinking define your approach to likelihood of these stresses.  In physical systems, the engineer knows how the system is supposed to be used and can plan for the stresses of those uses.  But in many cases, the systems designed and tested by engineers are not used in the conditions planned for or even for the exact uses that the engineer anticipated.

Sound familiar?

Human systems are not so fixed as physical systems.  Humans react to the system that they are experiencing and adjust their actions according to the feedback that they are receiving from the system.  So human systems will almost always change as they are used.

Human systems will almost always change as they are used.

That is what makes it so much more difficult to be a risk manager for a financial firm than for a firm that deals mainly with physical risks.  As noted above the humans that interface with the physical risks system do change and adapt, but there are usually a larger portion of possibilities that are fixed by the constraints of the physical systems.

With financial risks, the idea of adapting and using a type of transaction or financial structure for alternate purposes has become the occupation of a large number of folks who command a large amount of resources.

So if, for example, you are using a particular type of derivative to accomplish a fairly straightforward risk management purpose, it is quite possible that the market for that instrument will suddenly be taken over by folks with lots and lots of money, fast computers and turnover averages in the thousands per week.  Their entry into a market will change pricing and the speed of changes in pricing and then one day, suddenly, they will decide, perhaps little by little, but possibly all at once, to abandon that trade and the market will snap to being something different still.

The same sort of thing happens in insurance, but at a different speed.  Lawyers are always out there looking to “perfect” an argument to create a new class of claimants against different businesses and their insurers. THis results in a sudden jump in claims costs.

Interestingly, the strategies for those two examples might be the exact opposite.  It might be best to move on from the market that is suddenly overtaken by high speed hedge fund traders.  But the only way to recover extra losses from a newly discovered and “perfected” cause of tort is to stay with the coverage.

But in all cases, the risk manager is faced with the problem of overcoming Momentum Risk.  Convincing others that something that is not broken needs attention and possibly even fixing.

Risk Regimes

Lately, economists talk of three phases of the economy, boom, bust and “normal”. These could all be seen as risk regimes. And that these regimes exist for many different risks.

There is actually a fourth regime and for many financial markets we are in that regime now. I would call that regime “Uncertain”. In July, Bernanke said that the outlook for the economy was “unusually uncertain”.

So these regimes would be:

• Boom – high drift, low vol
• Bust – negative drift, low vol
• Normal – moderate drift, moderate vol
• Uncertain – unknown drift and unknown vol (both with a high degree of variability)

So managing risk effectively requires that you must know the current risk regime.

There is no generic ERM that works in all risk regimes.  And there is no risk model that is valid in all risk regimes.

Risk Management is done NOW to impact on your current risk positions and to manage your next period potential losses.

So think about four risk models, not about how to calibrate one model to incorporate experience from all four regimes.  The one model will ALWAYS be fairly wrong, at least with four different models, you have a chance to be approximately right some of the time.

Simplicity Fails

Usually the maxim KISS (Keep it Simple Stupid) is the way to go.

But in Risk Management, just the opposite is true. If you keep it simple, you will end up being eaten alive.

That is because risk is constantly changing. At any time, your competitors will try to change the game trying to take the better risks and if you keep it simple and stand still, leaving you with the worst risks.

If you keep it simple and focused and identify the ONE MOST IMPORTANT RISK METRIC and focus all of your risk management systems around controlling risk as defined by that one metric, you will eventually end up accumulating more and more of some risk that fails to register under that metric.  See Risk and Light.

The solution is not to get better at being Simple, but to get good at managing complexity.

That means looking at risk through many lenses, and then focusing on the most important aspects of risk for each situation.  That may mean that you will need to have different risk measures for different risks.  Something that is actually the opposite of the thrust of the ERM movement towards the homogenization of risk measurement.  There are clearly benefits of having one common measure of risk that can be applied across all risks, but some folks went too far and abandoned their risk specific metrics in the process.

And there needs to be a process of regularly going back to what your had decided were the most important risk measures and making sure that there had not been some sort of regime change that meant that you should be adding some new risk measure.

So, you should try Simple at your own risk.

It’s simple.  Just pick the important strand.

Forget you ever read this

I just read a great post that may be a key to understanding both the course of the economy and the public sentiment about government policies.

http://alephblog.com/2010/08/28/queasing-over-quantitative-easing-part-iii/

David Merkel suggests that people are unhappy with the unfairness of government policies.

I would go even further.  People have overwhelmingly come to the conclusion that they need to reduce their own debt.  Many, many people are enduring what they think of as hardship (by ceasing to spend money that they do not have) to bring down their personal debt level.  At the same time, they see the government adding to the public debt.  People are not stupid.  They think of the government debt as THEIR debt.  So they are economizing for naught if for every dollar of debt that they reduce, the government adds a dollar of debt.

Perhaps some day someone will discover a Pelzman effect like mechanism at work with regard to debt.  During the credit bubble that led up to the financial crisis, people where almost totally insensitive to the level of debt.  But now there is a high degree of sensitivity.  So if people feel that there is a right level of debt, then they will take actions to get to that level.  If the government works against that effort, then they will adjust their personal targets so that the aggregate of personal and public debt comes into line with their perceived optimal level of debt.  This may not be because they set a specific target, but because the rising level of public debt makes people uncomfortable and they express that discomfort in the way of looking for more security and less debt.

So maybe the extreme cutting of government spending that is happening in the UK is what is actually needed.  Because the government actions do not exist in some “all things being equal” economics textbook argument.  Government policy and the economy exist in and among the people of the world.

It is widely known that household consumption is a large fraction and major driver of the GDP in the US.  So if we want the GDP to grow, we need to do the things that will get households spending again.  Not in an all things being equal world, but in the real world with real people.

An interesting wrinkle to this is that the Keynesian ideas of using government spending to get out of the Great Depression did not work until the spending for WWII came along and the war spending did the trick.  Some economists have suggested that showed that a larger amount of government spending was needed than was tried prior to WWII.  But the ideas above – that the government spending will not be effective if the people want to save may have kept the earlier spending from working.  The spending for WWII may have worked though BECAUSE people thought of that spending as having its own merits.  WWII spending was necessary.

So perhaps government spending cannot go against the grain of public sentiment to overcome the Paradox of Thrift, unless the public believes that the spending is necessary.

And now to finally link this discussion to risk and risk management…

Perhaps Greenspan is right when he says that he did not know how to pop a bubble.  Maybe that is because he did not believe that he could have changed public opinion about the value of an asset class.  He could take actions that might temporarily hurt the value of an asset class, but it was quite possible that the public attitude would swing right back and keep inflating the bubble in spite of the actions of the Fed.

The same thing certainly has been true within firms.  When the risk manager finds him/her self at odds with the prevailing idea in a firm about a risk, he/she is more likely to lose their job than to change the prevailing opinion.

So in all three cases, in the general economy in severe recession, in an asset bubble and in a company overconfident about a particular risk. the only actions that can be effective will be actions that are not obviously going against the grain.  The actions will need to be designed so that they appear to go with the popular sentiment even when they are really intended to change the fundamentals with regard to that sentiment.

So, I now realize that I need to keep this secret.  So please forget you ever read this.

Biased Risk Decisions

The information is all there.  We have just wrapped it in so many technical terms that perhaps we forget what it is referring to.

Behavioral Finance explains exactly how people tend to make decisions without models.  They call them Biases and Heuristics.

This link is to one of my absolute favorite pages on the entire internet.  LIST OF COGNITIVE BIASES Take a look.  See if you can find the ways that you made your last 10 major business decisions there.

Now models are the quants ways to overcome these biases.  Quants believe that they can build a model that keeps the user from falling into some of the more emotional cognitive biases, such as the anchoring effect.  With a model, for example, anchoring is avoided because the modeler very carefully gives equal weight to many data points instead of more weight to the most recent data point.

But what the quants fail to recognize is that models strengthen some of the biases.  For example, models and modelers often fall under the Clustering illusion, finding patterns and attributing statistical distributions to data recording phenomena that has just finished one phase and is about to move on to another.

Models promote the hindsight bias.  No matter how surprising an event is at the time, within a few years, the data recording the impact of the event is incorporated into the data sets and the modelers henceforth give the impression that the model is now calibrated to consider just such an event.

And in the end, the model is often no more than a complicated version of the biases of the modeler, an example of the Confirmation Bias where the modeler has constructed a model that confirms their going in world view, rather than representing the actual world.

So that is the trade-off, between biased decisions with a model and biased decisions without a model.  What is a non-modeling manager to do?

I would suggest that they should go to that wikipedia page on biases and learn about their own biases and also sit down with that list with their modeler and get the modeler to reveal their biases as well.

Fortunately or unfortunately, things in most financial firms are very complicated.  It is almost impossible to get it right balancing all of the moving parts that make up the entirety of most firms without the help of a model.  But if the decision maker understands their own biases as well as the biases of the model, perhaps they can avoid more of them.

Finally, Jos Berkemeijer asks what must a modeler know if they are also the decision maker.  I would suggest that such a person needs desperately to understand their own biases.  They can get a little insight into this from traditional peer review.  But I would suggest even more than that they need to review the wiki list of biases with their peer reviewer and hope that the peer reviewer feels secure enough to be honest with them.

Not Complex Enough

Things changed and the models did not adapt.  But I am saying that is mostly because the models had no place to put the information.

With 20-20 hindsight, perhaps the models would have been better if instead of boiling everyone in one pot, you separated out folks into 5 or 10 pots.  Put the flippers into a separate pot.  Put the doctors into another pot.  (Did folks really believe that the no doc mortgages represented 10 times as many doctors than previously).  What about the no doc loans to contractors?  Wasn’t there a double risk there?  Put the people with LTV>100% in another pot.  Then model your 20% drop in prices.

And there was also no model of what the real estate market would do if there were 500,000 more houses than buyers.  Or any attempt to understand whether there were too many houses or not.

And the whole financial modeling framework has never had the ability to reflect the spirals that happen.

The models are just not complex enough for the world we live in.

Many are taught to look at a picture like the view above of the situation in Afghanistan and immediately demand that the picture be simplified.  To immediately conclude that if we draw a picture that complicated then it MUST be because we do not really understand the situation.  However, complexity like the above may be a sign that the situation is really being understood and that the model might just be complex enough to work as things change.

The idea that we will change the world so that the models work is tragically wrong headed.   But that is exactly the thinking that is behind most of the attempts at “reforming” the financial markets.  The thinking is that our models accurately describe the world when it is “normal” and that when our models are wrong it is because the world is “abnormal”.  So the conclusion is that we should be trying to keep the world in the normal range.

But the way that our models always fail is when the world makes a change, a non-linearity in the terminology of modelers.  The oft used analogy is the non-linearity that ruined the businesses of the buggy whip manufacturers.  They had a great model of demand for their product that showed how there was more demand every spring so that they put on extra shifts in the winter and rolled out the new models every April.

Then one April, the bottom fell out of their market.  That was because not only did those pesky horseless carriages cut into their businesses, but the very folks who bought the cars were the people who were always sure sales for new buggy whips each and every year.  That early adopter set who just had to have the latest model of buggy whip.

So we must recognize that these troubling times when the models do not work are frequently because the world is fundamentally changing and the models were simply not complex enough to capture the non-linearities.