In 2001 security agencies in the United States
proposed using prediction markets to assess the likelihood of a terrorist
attack. That government program was cancelled in 2003 due to criticism from US
Congress. Put forward a case for or against the use of markets to predict the
likelihood of this type of event.

Prediction markets are a platform for participants to trade in futures contracts where the payoff is dependent on an event rather than a value. This essay explores how neoclassical and behavioural economics support the use of this tool as a better predictor than traditional methods of polls and surveys. Using these markets to predict terrorist attacks in theory would retrieve information more easily from disparate sources (Wolfers and Zitzewitz, 2004)¹: both within security agencies and from the general public. Different design and participants created the moral argument against these markets. I will explore how they can be controlled and beneficial in contributing to anticipating terrorist attacks, especially cyber-terrorism.

As technology and social media is now
such a large part of western society there is another method of security threat
through the huge amount of data available. Although they monitor for key words,
prediction markets could be another means of picking out vital information from
the public domain. A lot of what is available could however be opinion as opposed
to informed knowledge (Tabarrok, 2002)², so a perfect market with traders
looking to make profit would ‘de-bias’ this information (Berg and Gruca, 2007)³.
The public information
may be a good contributor for topics that security agencies do not have in
depth knowledge of or it may just inform what the public is more interested in (Yeh, 2006)⁴.

A market needs uninformed
participants to form trades. If the market was formed, for example, of security
officers all from the same department then they would have common posterior
beliefs, not disagree and not want to take alternate sides of a trade (Aumann,
1976)⁵. It is therefore necessary for the market to at least be
across teams with different insider information. Yet these may still have the
same beliefs, so including the public could be a way to ensure a market is
diversified with those that are overconfident and speculating and those using
it for entertainment purposes. In the latter case, terrorism is a topic that
will attract a lot of interest from those looking to trade on new instruments
(Wolfers and Zitzeweitz, 2006)⁶. However, the US proposal for
prediction markets to test policy ideas and for geopolitical risks would not have
attracted much interest (Wolfers and Zitzewitz, 2004)¹. Therefore, a subsidy may have been used to
acquire participants. This generated public concern of people profiting in a
market (Hanson and Oprea, 2007)⁷ which
has negative moral implications.

Making a trade on a prediction market is with regards to
a specific event that may occur, but with cyber-terrorism there are thousands
more possible scenarios. Thus, it is a big challenge to speculate and form
clear and simple contracts to trade. However, it can be argued that markets
will do this more quickly and efficiently than the systems security units
already have. Also, once the platform and contracts are established they can be
replicated easily for many scenarios. A first use of prediction markets around
cyber-terrorism could be looking at the likelihood of a defence agency being
successfully penetrated (Yeh, 2006)⁴. This is a narrower and larger event
than the thousands of other possibilities across institutions, locations and
methods. There may be more knowledge on this already too because of the highly
sophisticated systems which need to be implemented and controlled, as well as
the interconnectivity of security and defence.

The Efficient Market⁸ and the Hayek hypotheses⁹ are
the main theories behind prediction markets aggregating information in a
sustainable manner. These tools allow the model to accurately price knowledge
from different individuals, as well as on the environment. From these come the
random walk theory. This is a caveat to the use of prediction markets, stating
that: because information is reflected instantaneously it cannot predict the
future movements, rather tomorrows price will simply reflect tomorrows
unpredictable news (Yeh, 2006)⁴.  Despite this, prediction markets that are
already in use have a high success rate. This is possibly because movements in
prices reveal events occurring, (Yeh,
2006)⁴ even if they have no contribution as a predictor for
future movements. Despite the confidence intervals from random walk projections
increasing over a longer time horizon, they can be argued to be more accurate
than the margins of error from a poll (Berg,
Nelson, and Rietz, 2003)¹⁰. Therefore, prediction markets are still beneficial
in measuring the degree of (un)certainty.

There aren’t many experts on computer programs and hacking so the
prediction market created around cyber-terrorism would be thinner than those
which back up the Efficient Capital Market hypothesis (Grady and Parisi, 2006)¹¹.
In this case, there would be less speculation and more price volatility because
the number of buyers for contracts and price they are looking for does not
match the (number of) sellers (Hanson and
Oprea, 2007)⁷. It is important for security agencies to understand if bubbles
will occur in these prediction markets on terrorism contracts because it could
damage their forecasts. As with asset markets, they should be equipped to
decide whether it is better to burst the bubble or respond after to limit
negative effects on the structure of the market (Yeh,
2006)⁴.

Any attempt to increase market participation to limit volatility as well as acquire more information should be done so with discretion due to the argument that potentially terrorists could enter, influence and even profit from the market changes. They could manipulate what the markets are formed from (coding in websites or software programs for example), thus changing prices reflected in the markets or simply by speculating in the market (Wolfers and Zitzeweitz, 2006)⁶. However, studies around this have shown that a trader whose target is uncertain and is trying to manipulate the market will not be successful in doing so because this extra noise (biases discussed earlier) gives more opportunity for profit making (Hanson and Oprea, 2007)⁷. This will bring prices to their correct equilibrium. In other words, trade reveals objectives of participants that are trading against or changing the market which in the case for terrorism can be supporting evidence. There is a trade-off between allowing the public to participate for added insight then recognising when a market manipulation is of any significance (i.e. how much the share price needs to deviate by before being an indicator of terrorist activity) and limiting participation which could just make the market more complicated with unknown benefits in prediction. It can be argued however that limiting participation is also not worthwhile because if it was terrorist’s intention to fund operations through the market they would manipulate a related market. This may have been seen pre-9/11 with unusual trading in airline stock but it is unclear whether adverse profits were made from this (Wolfers and Zitzewitz, 2004)¹.

Behavioural economics also introduces biases which could result in
prices being reflected incorrectly in prediction markets. They are based on
contracts on future events so, prices which arise are typically interpreted as
probabilities an event will occur, which has been discussed by Manski (2006)¹²to be unsound reasoning because there
would need to be more insight into why the trade has happened (Yeh, 2006)⁴. Nonetheless,
according to Kahneman and Tversky’s Prospect theory (1979)¹³ these
probabilities will be over-weighted when calculating rare vents such as
terrorism (Wolfers and Zitzeweitz, 2006)⁶. Yet, cyber-terrorism
is becoming much more frequent so this argument may reverse soon and studies
show this has little influence on the reliability of these instruments anyway
(Wolfers and Zitzewitz, 2005)¹⁴.

Similarly to how many of the public
think it is unethical that
bankers place large bets on financial markets, it has also been shown they view
prediction markets as a novel idea (Hanson and Oprea, 2007)⁷.
They may think it is a waste of taxpayer’s money (Yeh, 2006)⁴ to
create similar models. Increased education for the public on how markets work
will make the potential process less obscure. One view of prediction markets for terrorism is that
they are putting values on people’s lives and a profit can be made, even by the
attackers, which is immoral. Understanding
of how to control the use and misuse should also reduce opposition to
prediction markets. If a profit is made but results in detection of the threat (Wolfers and
Zitzewitz, 2004)¹ and lives being saved this will be
worthwhile. It should also be questioned whether it differs from the wide spread use of life
insurance. In fact, the UK has a market for insurance against cyber-terrorism
which is effectively pricing the likelihood, very similar to a prediction
market.

The possibility and scale of
potential cyber-attacks is a relatively new and obscure concept compared to the
visible consequences of a physical terrorist attack. An awareness of the benefits
of an extra resource for security in this new area could reduce opposition to
prediction markets. Perhaps trialling these markets with a different name to
see the participation and success rates could be a good start for introducing
prediction markets but this is ethically dubious.

In conclusion, I support the use of
prediction markets as another security measure. There needs to be a lot more
persuasion of the value of results as they are relatively new tool generally.
Then they may be accepted in the security industry and senior officers will aid
their effectiveness (Yeh, 2006)⁴. With gaps in our abilities as humans
to process data (Tabarrok, 2002)², prediction markets supplement
solutions such as surveys and consultants. They
could be extremely useful in aggregating and updating information over the long
term (Yeh, 2006)⁴ where this work
is cumbersome and time consuming.
This will allow expert judgement to be put to better use on deeper analysis.

Bibliography

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