Select an effective current health policy that focuses on or affects population health. What components of this policy make it effective? Conduct research on its history and the factors that influenced its development.
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>GET ANSWER