The destruction of prudence: understanding the global financial crisis

Prof. Russ Roberts of George Mason University has produced the best single analysis (pdf) (via) of the global financial crisis (GFC) I have read: empirically grounded, with careful, perceptive analysis leavened with moral outrage. (And if you do not have some level of moral outrage, you do not know what went on.)

Over the last 15 years or so, I have had various conversations with people about how the IMF has been injecting moral hazard into the international financial system by acting as “welfare for Wall St” and bailing out banks who loan to developing countries: bailouts which have usually involved major costs to the taxpayers of said countries.

This turns out to be a more general problem. President Bush put it nicely in conversation with the Chairman of the Federal Reserve and his Treasury Secretary:

Someday you guys are going to have to tell me how we ended up with a system like this. I know this is not the time to test them and put them through failure, but we’re not doing something right if we’re stuck with these miserable choices.

(I wonder if he did ever get a good answer.)

Roberts is about finding underlying causes:

Yes, deregulation and misregulation contributed to the crisis, but mainly because public policy over the last three decades has distorted the natural feedback loops of profit and loss. As Milton Friedman liked to point out, capitalism is a profit and loss system. The profits encourage risk taking. The losses encourage prudence. When taxpayers absorb the losses, the distorted result is reckless and imprudent risk taking.

In other words, a system not merely suffering from moral hazard, but increasingly built on it:

The most culpable policy has been the systematic encouragement of imprudent borrowing and lending. That encouragement came not from capitalism or markets, but from crony capitalism, the mutual aid society where Washington takes care of Wall Street and Wall Street returns the favor. Over the last three decades, public policy has systematically reduced the risk of making bad loans to risky investors. Over the last three decades, when large financial institutions have gotten into trouble, the government has almost always rescued their bondholders and creditors. These policies have created incentives both to borrow and to lend recklessly.

At the level of social systems, there is a very large difference between reducing risk and suppressing risk. Reducing risk means one is lowering the level of risk in the general system. One can see that happening over time with the downward historical trends in interest rates, for example.

Suppressing risk
Suppressing risk shifts the risk from certain actions (and thus particular agents) to somewhere else: generally into more concentrated forms in the future. From the beginning of Federation until the early 1980s, Australia was under the Deakinite policy regime of industry protection, wage arbitration, state paternalism, white Australia and imperial benevolence.

Each part of the Deakinite regime was intended to be about reducing risk. Industry protection blocked foreign competition, wage arbitration blocked (low) wage competition, state paternalism reduced risks of aging and ill-health, white Australia blocked competition from “tropical zone” migrants and imperial benevolence sought the protection of friendly Great Power (first Britain, then the US). White Australia was the first part of the policy regime to disappear, being abandoned in the 1966-72 period. It arose because, during the nineteenth and first part of the twentieth century, there were two global labour flows. “Temperate zone labour” from Europe to North America and the Antipodes and “tropical zone labour”, particularly from China and India to various European colonies. It was an urgent demand of the North American and Antipodean working class that it be shielded from competition from “tropical zone labour”, often using the available prop of racial ideology.

As a policy regime, the Deakinite policy regime suppressed risk rather than reducing it in any systemic sense. And it suppressed risk by building a lot of rigidities into the economy. Consequently, the Australian economy handled external economic shocks badly because it lacked flexibility. The greatest achievement in the dismantling of most of the Deakinite policy regime has to been create a much more flexible Australian economy, which has handled both the 1997 Asian Crisis and the more recent GFC and Great Recession not merely well, but better than comparable countries.

[Under the Deakinite regime, regulation and other government intervention blocked the flow of information by blocking action: which inhibited the development of better ways of doing things and effective action in response to sudden changes. The latter in particular led to the Australian economy tending to deal poorly with economic shocks in the period.]

What the history of bailouts and implicit government guarantees that Roberts sets out did is suppressed risk by reducing risk for individual actors thereby massively increasing the level of risk in the financial system as a whole. Suppressing risks for individual acts, which therefore discouraged attention to risk, massively increased the level of systemic risk. [The pattern of action did not reflect the risks inherent in particular investments, as it suppressed seeking such information and acting upon it. What it actually reflected was the implicit government guarantees. Taxpayers were the "backstops" for the actions of others.]

Destroying prudence
Roberts sets out the perverse incentives created:

The punishment of equity holders is usually thought to reduce the moral hazard created by the rescue of creditors. But it does not. It merely masks the role of creditor rescues in creating perverse incentives for risk taking.

These perverse incentives became pervasive, which is precisely the problem:

Because of the large amounts of leverage—the use of debt rather than equity—executives can more easily generate short-term profits that justify large compensation. While executives endure some of the pain if short-term gains become losses in the long run, the downside risk to the decision-makers turns out to be surprisingly small, while the upside gains can be enormous. Taxpayers ultimately bear much of the downside risk. Until we recognize the pernicious incentives created by the persistent rescue of creditors, no regulatory reform is likely to succeed.
Almost all of the lenders who financed bad bets in the housing market paid little or no cost for their recklessness. Their expectations of rescue were confirmed.

Hence:

And what we do is make it easy to gamble with other people’s money—particularly borrowed money—by making sure that almost everybody who makes bad loans gets his money back anyway. The financial crisis of 2008 was a natural result of these perverse incentives.

Roberts uses the analogy of a poker game with Uncle Sam watching (sometimes changing the rules, sometimes guaranteeing players) to illustrate the problems very effectively.

What was going on was not “managing” the market system, it was a profound subverting of it:

Capitalism is a profit and loss system. The profits encourage risk taking. The losses encourage prudence. Eliminate losses or even raise the chance that there will be no losses and you get less prudence. So when public decisions reduce losses, it isn’t surprising that people are more reckless.

Read More...There is also another factor which is implicit in Roberts’ analysis, but he does not bring out. If access to decision-makers increases one’s returns, that will advantage firms with access to decision-makers. The flooding of funds into the financial system and financial markets is hardly surprising in the circumstances. An expansion particularly driven via the underpinning of massive levels of leveraging. Thus:

Without extreme leverage, the housing meltdown would have been like the meltdown in high-tech stocks in 2001—a bad set of events in one corner of a very large and diversified economy.

This was a crisis building for a long time:

The [1984] rescue of Continental Illinois and the subsequent congressional testimony sent a signal to the poker players and those that lend to them that lenders might be rescued.

A signal that was reinforced again and again by subsequent government action:

Continental Illinois was just the largest and most dramatic example of a bank failure in which creditors were spared any pain. Irvine Sprague, in his 1986 book:

“Of the fifty largest bank failures in history, forty-six—including the top twenty—were handled either through a pure bailout or an FDIC assisted transaction where no depositor or creditor, insured or uninsured, lost a penny.”

The 50 largest failures up to that time all took place in the 1970s and 1980s. As the savings and loan (S&L) crisis unfolded during the 1980s, government repeatedly sent the same message: lenders and creditors would get all of their money back. Between 1979 and 1989, 1,100 commercial banks failed. Out of all of their deposits, 99.7 percent, insured or uninsured, were reimbursed by policy decisions.

This had utterly predictable effects on the attention of actors in the financial system to levels of risk:

… all profit and no loss make Jack a dull boy.

[The information role of markets and prices were systematically distorted.] It was the systematic destruction, by government policy, of prudence in the financial system:

Each case seems different. But there is a pattern. Each time, the stockholders in these firms are either wiped out or see their investments reduced to a trivial fraction of what they were before. The bondholders and lenders are left untouched.

Tracing the effects of this does have some evidentiary problems. No one is likely to say “yes, I was reckless!” But:

While direct evidence is unlikely, the indirect evidence relies on how people generally behave in situations of uncertainty. When expected costs are lowered, people behave more recklessly.

This was a problem not only in the US. The UK authorities were playing the same game:

The only difference between this scenario in the United Kingdom and the one in the United States is that in the U.S. the Fed came to the rescue and the executives, for the most part, kept their bonuses.

That equity was not guaranteed made much less of a difference than one would imagine. As Roberts points out, equity investors tend to diversify and could use bonds to reduce downside risk.
Read More...
Roberts cites a study on the perverse incentives facing executives and concludes that:

This kind of looting and corruption of incentives is only possible when you can borrow to finance highly leveraged positions. This in turn is only possible if lenders and bondholders are fools—or if they are very smart and are willing to finance highly leveraged bets because they anticipate government rescue.

He then considers the case of a couple of the CEOs whose firms were rescued by the government (i.e. the taxpayer):

When we look at Cayne and Fuld, it is easy to focus on the lost billions and overlook the hundreds of millions they kept. It is also easy to forget that the outcome was not preordained. They didn’t plan on destroying their firms. They didn’t intend to. They took a chance. Maybe housing prices plateau instead of plummet. Then you get your $1.5 billion. It was a roll of the dice. They lost.
When Cayne and Fuld were playing with other people’s money, they doubled down, the ultimate gamblers. When they were playing with their own money, they were prudent. They acted like bankers. (Or the way bankers once acted when their own money or the money of their partnership was at stake.)

In other words, behaviour was different in the realms were public policy was not systematically destroying prudence.

Roberts examines various explanations offered for the GFC, concluding that:

These explanations all have some truth in them. But the undeniable fact is that these allegedly myopic and overconfident people didn’t endure any economic hardship because of their decisions. The executives never paid the price. Market forces didn’t punish them, because the expectation of future rescue inhibited market forces. The “loser” lenders became fabulously rich by having enormous amounts of leverage, leverage often provided by another lender, implicitly backed with taxpayer money that did in fact ultimately take care of the lenders.

The systematic destruction of prudence as moral outrage.

Perverse incentives
Roberts then examines the (largely regulation driven) perverse incentives in housing finance and purchase in various markets before examining Fannie Mae and Freddie Mac:

But between 1998 and 2003, Fannie and Freddie played an important role in pushing up the demand for housing at the low end of the market. That in turn made subprime loans increasingly attractive to other financial institutions as the prices of houses rose steadily.

Fannie and Freddie had particular value for policy makers:

… one other group sitting at the table playing with other people’s money: politicians. Politicians are always eager to spend other people’s money. It’s what they do for a living. But it’s an even better deal for politicians if they can hide the fact that they’re spending other people’s money or delay when the bill comes due. That’s what they did with Fannie and Freddie.

We can see clearly from Roberts’s analysis that the suppression of risk actually massively increased the total level of risk in the system. In the housing market, there were massive increases in riskier housing loans.

The story Roberts tells is one of consistent rationality creating perverse incentives, rather than having to posit various levels of irrationality. In particular, he asks the excellent question of why specific types of assets were invested in and not others.

Roberts shows quite clearly that the issue is not merely problems with computer models used in assessing risks, it is the incentives driving the creation and use of models.

How was prudence destroyed? By making the taxpayers the backstop, the guarantors of what wealthy, powerful and connected people were doing:

As in the Fannie and Freddie story, the firms aren’t the real financers of the salaries associated with picking up nickels. The taxpayers ultimately fund picking up of nickels, and the taxpayers get flattened.

In Yoram Barzel’s property rights analysis of firms, the boundary of the firm is the boundary the guarantee offered by the equity capital. The owner of the risk exposure in lending is whoever provides the debt guarantee. That turned out to be the taxpayers. The system systematically nationalised risk, and operated as chaotically as any command economy does. [For it had the same underlying structure: those making the decisions about capital did not have to bear the costs of what they did while information was being systematically blocked or distorted.]

In particular, it destroyed prudence.

Bloating perversity
Part of what went on was the creation of complex financial instruments whose attributes were not clear to the “owners” of said instruments. But, if someone else is providing the ultimate guarantee, how hard are you going to bother to look (or even worry)?

Roberts’ analysis leads him to a very scary place:

An unpleasant but unavoidable conclusion of this paper is that Wall Street was (and remains) a giant government-sanctioned Ponzi scheme.

But, if one wants to know how the financial system became so huge, then this analysis explains why. The advantages of access to policy makers both causing, and combining with, the implicit (and increasingly explicit) taxpayer guarantee. As Roberts notes:

There is an old saying in poker: If you don’t know who the sucker is at the table, it’s probably you. We are the suckers. And most of us didn’t even know we were sitting at the table.

This is not a good place for democracy to be:

Rescuing rich people from the consequences of their decisions with money coming from average Americans is bad for democracy.

Just as the IMF has been bad for global governance because it has been doing the same thing, only more so, and gouging taxpayers a lot poorer than the average American taxpayer.

The consequences for democracy are indirect, the consequences for the economy much more direct:

Rescuing people from the consequences of their decisions is bad for capitalism.

What we do not need is replacing “bad” people with “good” ones. We need better ideas and much better public policy incentives.

Maybe it is as simple, as the cases of Canada and Australia suggest, of just having good prudential regulation in the first place. (A model, which does not, alas, work for the IMF.)

The massive destruction of prudence, and the subsequent creation of high levels of systemic risk (including sovereign debt issues—themselves an issue of a lack of fiscal prudence—which Canada and Australia have also managed to avoid), perhaps explain the fearfulness of monetary and banking authorities, such as the Bank for International Sentiments recently endorsing fiscal and monetary tightening in a situation of serious deflationary pressures. (Since one would have to be something of an obsessive idiot to be worrying about inflation in the current situation: though (re)fighting the last war you won has notoriously held a certain attraction.)

Roberts quotes Milton Friedman putting the point well about not just hankering for the “right” people:

The way you solve things is to make it politically profitable for the wrong people to do the right things.

That is, after all, the way Madison designed the constitutional structure of the American Republic. What is needed is ways of discouraging politicians from continuing to systematically destroy prudence in the financial system.

ADDENDA: Roberts' analysis can be taken as something of a case study for Jeffrey Friedman's argument (pdf) (via) for the cognitive superiority of markets over politics.

FURTHER ADDENDA: Having now read Hayek's The Meaning of 'Competition' (which, Jeffrey Friedman is completely correct, should be read in conjunction with Hayek's The Uses of Knowledge in Society), I have extended the post somewhat by the sections in [square brackets].

Computer models and cognitive failure

[Note: as useful new links turn up, I will update this post.]

Even before computers, there were problems with models. A physicist's testimony to the US House of Representatives on climate change (pdf) provides a lovely historical example:

Modelers have been wrong before. One of the most famous modeling disputes involved the physicist William Thompson, later Lord Kelvin, and the naturalist Charles Darwin. Lord Kelvin was a great believer in models and differential equations. Charles Darwin was not particularly facile with mathematics, but he took observations very seriously. For evolution to produce the variety of living and fossil species that Darwin had observed, the earth needed to have spent hundreds of millions of years with conditions not very different from now. With his mathematical models, Kelvin rather pompously demonstrated that the earth must have been a hellish ball of molten rock only a few tens of millions of years ago, and that the sun could not have been shining for more than about 30 million years. Kelvin was actually modeling what he thought was global and solar cooling. I am sorry to say that a majority of his fellow physicists supported Kelvin. Poor Darwin removed any reference to the age of the earth in later editions of the “Origin of the Species.” But Darwin was right the first time, and Kelvin was wrong. Kelvin thought he knew everything but he did not know about the atomic nucleus, radioactivity and nuclear reactions, all of which invalidated his elegant modeling calculations.

One of the more mordantly amusing aspects of the current credit crisis is the massive failure of relying on computer models for assessing risk. A failure that was quite comprehensive:

In fact, most Wall Street computer models radically underestimated the risk of the complex mortgage securities … The people who ran the financial firms chose to program their risk-management systems with overly optimistic assumptions and to feed them oversimplified data. This kept them from sounding the alarm early enough.

Paul Volker has been publicly scathing about such financial engineering. A make-believe universe where spreadsheets were oracles and bigger and better computer models bigger and better oracles.

Even worse, under Basel II computer models became part of the regulatory framework:

Instead of applying a uniform standard (such as a specific debt to equity ratio) to all financial institutions, Basel II contemplated that each regulated financial institution would develop its own individualized computer model that would generate risk estimates for the specific assets held by that institution and that these estimates would determine the level of capital necessary to protect that institution from insolvency. But in generating this model and crunching historical data to evaluate how risky its portfolio assets were, each investment bank gave itself a discretionary opportunity to justify higher leverage. Because each model was ad hoc, specifically fitted to a unique financial institution, no team of three SEC staffers was in a position to contest these individualized models or the historical data used by them. Thus, the real impact of the Basel II methodology was to shift the balance of power in favor of the management of the investment bank and to diminish the negotiating position of the SEC's staff. Basel II may offer a sophisticated tool, but it was one beyond the capacity of the SEC's largely legal staff to administer effectively.

Financial institutions used highly sophisticated computer models, put together by highly-paid people using masses of data based on what was taken to the most up-to-date understanding of how things work. All of which gave the output of the models huge credibility.

The problem was precisely that they had such credibility. In particular, their output was treated as empirical evidence: as telling people about the state of their risk exposure.

They did nothing of the kind. All they did—all computer models can ever do—is tell you the consequences of your premises, both empirical and analytical/causal. They do not tell you about how the world is. They tell you about how you think the world is. One can then test your thinking about the world by comparing what your model(s) churn out to how the world turns out to be.

But this is a distinction that is very, very easy to lose sight of. Particularly given the effort and analytical power put into creating the things and their “black box”—facts in one end, results out the other end—nature.
Read More...
A piece on why economists failed to predict the crash makes some very pertinent points about the problems in the use of computer models. First was the allure of new technology with the demands of the flashy new toys overwhelming empirical difficulties:

As computers have grown more powerful, academics have come to rely on mathematical models to figure how various economic forces will interact. But many of those models simply dispense with certain variables that stand in the way of clear conclusions, says Wharton management professor Sidney G. Winter. Commonly missing are hard-to-measure factors like human psychology and people's expectations about the future, he notes.

Which led to major problems, as a recent report has noted:

"The economics profession appears to have been unaware of the long build-up to the current worldwide financial crisis and to have significantly underestimated its dimensions once it started to unfold," they write. "In our view, this lack of understanding is due to a misallocation of research efforts in economics. We trace the deeper roots of this failure to the profession's insistence on constructing models that, by design, disregard the key elements driving outcomes in real world markets."
The paper, generally referred to as the Dahlem report, condemns a growing reliance over the past three decades on mathematical models that improperly assume markets and economies are inherently stable, and which disregard influences like differences in the way various economic players make decisions, revise their forecasting methods and are influenced by social factors.

The prevalence of the use of the flashy new tools led to their own problems:

When certain price and risk models came into widespread use, they led many players to place the same kinds of bets, the authors continue. The market thus lost the benefit of having many participants, since there was no longer a variety of views offsetting one another. The same effect, the authors say, occurs if one player becomes dominant in one aspect of the market. The problem is exacerbated by the "control illusion," an unjustified confidence based on the model's apparent mathematical precision, the authors say. This problem is especially acute among people who use models they have not developed themselves, as they may be unaware of the models' flaws, like reliance on uncertain assumptions.

But a model is based on judgments about what matters and what does not, which is why empirical testing is essential:

Much of the financial crisis can be blamed on an overreliance on ratings agencies, which gave complex securities a seal of approval, says Wharton finance professor Marshall E. Blume. "The ratings agencies, of course, use models" which "grossly underestimated" risks.
"Any model is an abstraction of the world," Blume adds. "The value of a model is to provide the essence of what is happening with a limited number of variables. If you think a variable is important, you include it, but you can't have every variable in the world.... The models may not have had the right variables."
The false security created by asset-pricing models led banks and hedge funds to use excessive leverage, borrowing money so they could make bigger bets, and laying the groundwork for bigger losses when bets went bad, according to the Dahlem report authors.

A piece on failures of bank stress testing (pdf) puts the problem of such computer models very clearly:

Of course, all models are wrong. The only model that is not wrong is reality and reality is not, by definition, a model. But risk management models have during this crisis proved themselves wrong in a more fundamental sense. They failed Keynes’ test – that it is better to be roughly right than precisely wrong. With hindsight, these models were both very precise and very wrong.

To repeat: computer models are a way of testing your assumptions (as one statistician said, no model is true but some are useful) by checking the results of such simulations against the evidence, they are not evidence of how the world is. At best, in engineering models for example, one can say this is a system we know very well, and a model which we have tested a lot, so based on its very well-established record of extreme accuracy, we have run this proposed new structure in the model and ... In such cases, we have a very useful—because it is much tested based on a vast array of cases and knowledge about cases—model. But our much-hyped climate models are nowhere near that stage. Nor, clearly, are financial or economic models.

There are good reasons to be sceptical of fiscal stimulus as an anti-recession tool. But the Obama Administration's offered support for its amazing fiscal proposals do provide a wonderful example of the (mis)use of computer models--the computer model as modern policy oracle tool:

The Administration's estimates (pdf) for the effect of a stimulus plan cite no new evidence and no theory at all for their large multipliers. The multipliers come ".. from a leading private forecasting firm and the Federal Reserve’s FRB/US model." (Appendix 1) Multipliers are hard-wired in these models by assumption, rather than summarizing any evidence on the effectiveness of fiscal policy, and the models reflect the three theoretical fallacies above. The multipliers in this report are not conditioned on "slack output" or something else -- they state that every dollar of government spending generates 1.57 dollars of output always! If you've got magic, why not 2 trillion dollars? Why not 10 trillion dollars? Why not 100 trillion, and we can all have private jets? If you don't believe that, why do you think it works for a trillion dollars? Their estimates of industry effects come from a blog post (p. 8)! Ok, they did their best in the day and a half or so they had in the rush to put the report together. But really, before spending a trillion dollars of our money, wouldn't it make sense to spend, say one tenth of one percent on figuring out if it will work at all? (That would be 100 million dollars, more than has ever been spent on economic research in the entire history of the world.)

Which really is the modern equivalent of consulting the sheep’s entrails. (But it must be science, they used a computer! And mathematics! Together!)

Many of the financial models have, as a reader pointed out in a private email, the worse problem

they made up their own [models], in secret and separate from each other (they contain confidential data that could provide competitive leverage to their opposites)

so lacked outside checkability. Such lack of outside ability to check makes things worse, but allowing it does not solve the inherent problems of computer models being models.

That computer models were apparently allowed to set regulatory standards is simply horrifying and shows the extent of the cognitive failure, the failure in basic understanding of what computer models are and what they can do. Even the above quote on the use of computer models in regulation under Basel II misunderstands the problem, treating it as the SEC being "outclassed" (no doubt true) rather than a completely inappropriate use of computer models based on a profound misunderstanding of what they can and can not do and in what circumstances.

Moving on from proprietary financial models, consider this rather good Economist article on general economic models. Which is, and has been, more intensively studied: how economies work or how the world's climate works? (Think Central Banks, Treasuries, Banks, Investment Houses, Faculties, Consultancies ...) Which has more financial and other returns people with money and power care about riding on getting things right? OK, so which models do you think generally perform better: general economic models or climate models? (If you answered “[whichever,] but it makes no difference”, go to the top of the class.) More to the point, have all these resources and cognitive effort poured into economic and financial models provided any guarantee of success? Even given there are reasons to think it is inherently harder to model human systems than physical ones and macroeconomics does not even have a common analytical language? In the words of recent testimony before the US Congress:

Using models within economics or within any other social science, is especially treacherous. That’s because social science involves a higher degree of complexity than the natural sciences. The reason why social science is so complex is that the basic unit in social science, which economists call agents, are strategic, whereas the basic unit of the natural sciences are not. Economics can be thought of the physics with strategic atoms, who keep trying to foil any efforts to understand them and bring them under control. Strategic agents complicate modeling enormously; they make it impossible to have a perfect model since they increase the number of calculations one would have to make in order to solve the model beyond the calculations the fastest computer one can hypothesize could process in a finite amount of time.
Put simply, the formal study of complex systems is really, really, hard. Inevitably, complex systems exhibit path dependence, nested systems, multiple speed variables, sensitive dependence on initial conditions, and other non-linear dynamical properties. This means that at any moment in time, right when you thought you had a result, all hell can break loose.

Any analytical discipline that comes to rely on computer models is in deep, deep trouble: particularly if they are treated as providing empirical evidence. And the more there are things that matter in understanding some system that are poorly understood, the deeper the trouble.

In the case of the financial institutions, clearly the various financial instruments being modelled where not understood anywhere near as well as people thought. The modellers covered them, they just did not cover them accurately. So all the models ended up showing was (after fact) what their modellers did not understand: their “unknown unknowns” (i.e. they apparently did not know that they did not know). And a basic principle of modelling is, that if you do not understand it, you cannot model it.

The case is hardly better if one has known unknowns: things that you know that you do not know. Since one then has to put “patches” in one’s computer models to “guess” at their operation. Guesses that are unlikely to be accurate but which the existence of the computer model disguises them as being guesses. Indeed, it is expected that we will have the computing power to fully model a cup of coffee in about 10 years. Compare that level of difficulty in fluid dynamics to an entire climate.

Such as, for example:

... the oceans and their circulations are the thermal and inertial flywheels of the climate system; as the ocean circulation changes, the atmosphere and its climate respond. Our knowledge of subsurface ocean circulations and their variability is limited. Without this vital input, projections of future climate are tenuous at best.

Climate models vary quite a bit in their assumptions about carbon sensitivity but come up with much the same results:

The question is: if climate models differ by a factor of 2 to 3 in their climate sensitivity, how can they all simulate the global temperature record with a reasonable degree of accuracy.

Then there is "adjustment” of sea level data:

So it was not a measured thing, but a figure introduced from outside. I accused them of this at the Academy of Sciences in Moscow —I said you have introduced factors from outside; it's not a measurement. It looks like it is measured from the satellite, but you don't say what really happened. And they ans-wered, that we had to do it, because otherwise we would not have gotten any trend!
That is terrible! As a matter of fact, it is a falsification of the data set. Why? Because they know the answer. And there you come to the point: They “know” the answer; the rest of us, we are searching for the answer. Because we are field geologists; they are computer scientists. So all this talk that sea level is rising, this stems from the computer modeling, not from observations. The observations don't find it!

The sort of thing that might make an IPCC expert reviewer a bit annoyed:

My main complaint with the IPCC is in the methods used to "evaluate" computer models. ... This has become so complex that many have failed to notice that it has no scientific basis, but is just an assembly of the "gut feelings" of self-styled "experts". It has been developed to a complex web of "likelihoods", all of which are assigned fake "probability" levels.

As Australian economist David Clark used to say of econometrics:

if the data is sufficiently tortured it will confess.

This is not to knock climate science, still less empirical results. But if one is going to be sceptical about economic models, fine. But don't then turn around and worship climate models. They still can't work out how to handle clouds. Though they are getting better. This without getting into some of the weird and wonderful economic forecasting assumptions built into prominent climate models. And it does not help that we still cannot model the Sun and its activity properly:

"It turns out that none of our models were totally correct," says Dean Pesnell of the Goddard Space Flight Center, NASA's lead representative on the panel. "The sun is behaving in an unexpected and very interesting way."

In the words of Freeman Dyson:

The climate-studies people who work with models always tend to overestimate their models. They come to believe models are real and forget they are only models.

Modelling is a somewhat dodgy business. It does not become less dodgy simply because a "hard" scientist is modelling a really complex system. It may well be better to lean more on statistical extrapolation of a relatively simple and transparent sort than complex computer modelling. Particularly as the IPCC models do not do better than the "no-change" model:

The errors of the IPCC projection over the years 1992 to 2008 were little different from the errors from the no-change model, when compared to actual measured temperature changes. When the IPCC’s warming rate is applied to a historical period of exponential CO2 growth, from 1851 to 1975, the errors are more than seven times greater than errors from the no-change model.

A former NASA scientist is sceptical about anthropogenic global warming, in large part because he does not think climate models are worth much:

My own belief concerning anthropogenic climate change is that the models do not realistically simulate the climate system because there are many very important sub-grid scale processes that the models either replicate poorly or completely omit ... Furthermore, some scientists have manipulated the observed data to justify their model results. In doing so, they neither explain what they have modified in the observations, nor explain how they did it. They have resisted making their work transparent so that it can be replicated independently by other scientists. This is clearly contrary to how science should be done. Thus there is no rational justification for using climate model forecasts to determine public policy.

Someone with a lot of background in forecasting is also unimpressed with climate models. While this annoyed scientist not impressed about almost anything about AGW including the modelling. Models are sensitive to which specifications for key processes or variables are used.

Even a supporter of the climate models can suggest that they have been somewhat oversold:

But perhaps the greatest danger is climate scientists blatantly overselling what we know. That could bring everything down and cost the world valuable time.
Overselling what we know could bring climate science down.

Or simply acknowledging how much they are still a work in progress:

"Model biases are also still a serious problem. We have a long way to go to get them right. They are hurting our forecasts," said Tim Stockdale of the European Centre for Medium-Range Weather Forecasts in Reading, UK.

There are plenty of voices concerned about the problems with computer models in their application to climate science. And it is so easy to have some fun with sub-prime metaphors. James Lovelock, he of the Gaia hypothesis, is also worried that computer modelling is undermining science:

Gradually the world of science has evolved to the dangerous point where model-building has precedence over observation and measurement, especially in Earth and life sciences. In certain ways, modelling by scientists has become a threat to the foundation on which science has stood: the acceptance that nature is always the final arbiter and that a hypothesis must always be tested by experiment and observation in the real world.

It is notoriously difficult to find geologists who support CAGW:

I do not know one geologist who believes that global warming is not taking place. I do not know a single geologist who believes that it is a man-made phenomenon.

Geologist Ian Plimer is certainly not one of them. He has some things to say about the use of computer models:

Much of what we have read about climate change, he argues, is rubbish, especially the computer modelling on which much current scientific opinion is based, which he describes as "primitive".

More broadly, environmental “science” based on computer models has a track record – a poor one. (I was a bit annoyed to find that claims I had seen around a lot had been generated by computer models and I was not aware of that.) There are profound and inherent difficulties with modelling complex systems:

[p]erhaps the single most important reason that quantitative predictive mathematical models of natural processes on earth don't work and can't work has to do with ordering complexity. Interactions among the numerous components of a complex system occur in unpredictable and unexpected sequences.

Yet the models become talismans, impressive in their "power" and convenience:

environmental science finds itself caught in the grip of "politically correct modeling" (the authors' emphasis) in which there is enormous pressure on scientists, many of whom discover "that modeling results are easier to live with if they follow preconceived or politically correct notions." The models take on a life of their own, and become obstacles to conducting serious field studies that might strengthen our empirical grasp of ecosystem dynamics. "Applied mathematical modeling has become a science that has advanced without the usual broad-based, vigorous debate, criticism, and constant attempts at falsification that characterize good science ..."

Our climate models are not scientific predictions, still less projections. They are guesses, in the sense of conjectures rather than estimations. Partially informed, fairly sophisticated, guesses: but guesses. Guesses that them being computer models by clever people disguise being guesses. Exactly how many billions of dollars are such guesses worth the wager of? There may be a few folk on Wall St who can give you a heads up on that.

UPDATE See also this critique of models in general. Which is not the same as arguing that all models are known to be false.