Summary Of The 2004 Boxing Day Tsunami

Society is increasingly subjected to predictions on subjects as diverse as economic development, finance, fashion and even relationships. For instance, Economists forecast the gross domestic product of countries; Financial Analysts model the likely increase in earnings per share of a company based on potential sales of future products; Fashion forecasters predict how the mood of consumers determine the styles for next season’s haute couture collections; and websites encourage a person to input data about them self and an algorithm tries to predict their most suitable partner.

The quotation from Lao Tzu assumes that prediction and knowledge are mutually exclusive. Using the areas of knowledge of natural sciences and human sciences, this

essay explores whether mutual exclusivity is realistic or whether predictions have become more reliable as knowledge has increased. Knowledge can be defined as “information and skills acquired through experience, education or the theoretical and practical understanding of a subject” (Oxford Dictionaries English, 2018). Whereas prediction can be defined as a forecast or estimate that something will happen in the future or will be a consequence of something else. (Oxford Dictionaries English, 2018)

Natural sciences and human sciences were chosen because predictions concerning weather forecasts (from natural sciences) and criminal behaviour (from human sciences) have serious impacts on society and also global warming and the threat from terrorism are particularly relevant at this time.

The questions arise: Is it reasonable to expect that over time increased knowledge has led to improved predictions? Or, is it more reasonable that if one thinks they know, they cease to predict?

Natural Sciences will be considered regarding knowledgeable predictions. Biology, Chemistry, Physics all propose and develop theories that predict whether certain experiments and concepts will establish new knowledge. As an IB geography student the syllabus involves forecasting and prediction when learning about hazards and predicting disasters. This led to me researching the 2004 Boxing Day

Tsunami.

On the 26th of December, 2004, a magnitude 9.2 earthquake occurred in the Indian Ocean, just off the west coast of Sumatra, Indonesia. This was due to the Indo-Australian plate being subducted under the Eurasian plate. This earthquake caused the rising of the seafloor, displacing seawater above and creating a series of tsunami waves along the coasts of many countries in the Indian Ocean. Since the 1950’s researchers have been studying the movements in the earth’s plates and the effect of those movements. However, despite the increasing body of knowledge from the study of plate tectonics, prior to the 2004 earthquake, there was no warning system in the Indian Ocean to predict a tsunami. The largest earthquakes known previously in this area were no larger than magnitude 8 and therefore this earthquake took scientists by surprise.
Before 2004 nobody had prepared any predictions using the knowledge of the plate tectonic scientists. These days, Scientists and Meteorologists can predict the location of an earthquake but cannot foresee the timing. As an example, scientists predict that a catastrophic earthquake is due to impact San Francisco, but they cannot predict when it will occur.

Whilst pinpointing an earthquake is difficult, the formation of a tsunami can be detected, and its probable path predicted. After the 2004 tsunami, state-of-the art warning systems were installed, that have increased scientific knowledge and made forecasts more precise. Previously predictions were made from the magnitude of an earthquake about whether there would be a tsunami and the possible size. Now, forecasters also use data from buoys in the ocean. After seismometers sense an earthquake, “the buoys detect pressure changes from a tsunami.” After receiving that data, “accurate forecasts can be made within an hour.” (MICHAEL CASEY, 2014) This time will be reduced with more advances in scientific knowledge. Therefore, predictions are improved with increased knowledge and prediction and knowledge are not mutually exclusive. The question then arises as to whether it is possible to predict with only limited, non-specialist knowledge?

It could be expected that a reliable prediction of an event that requires knowledge of many complex systems (oceanography, seismology, etc) can only be made by someone with a thorough education in those subjects and access to significant knowledge articles to assist in predictive ability.
However, in the 2004 Tsunami, a girl on holiday with her family in Thailand had studied tsunamis in her geography lesson just two weeks previously. Although she had limited knowledge due to her age, when the tide went out quickly, she realised the cause must be a tsunami. By reaching her conclusion, her quick thinking led her to being able to warn people and save over one hundred lives.
This supports the claim that reliable predictions can be made with limited knowledge and challenging the statement of this essay, that those who predict, don’t have knowledge.

Another example occurred on the island of Simeulue, during the same disaster. This island was closest to the epicentre of the earthquake. The local indigenous people ran into the hills as soon as the earthquake occurred, thereby escaping the subsequent tsunami. These people have only traditional knowledge, in which language in their scriptures informed them that once they felt ground tremors they had to retreat inland to higher ground. This also shows that reliable predictions can be obtained with limited knowledge.
Is it possible to draw similar conclusions that a prediction can be reliable if made with only limited knowledge in Human Sciences?
An increasing challenge faced by societies across the world is the prediction of terrorist attacks. Can those involved in advancing the knowledge of human psychology and behavioural science predict the actions of individuals or groups of people that are determined to kill other members of society? Criminologists study the causes of terrorism and ways to prevent it. Psychologists study the individuals involved in incidents. Both disciplines use their different training to look for common threads in terrorists (real and potential), for example in upbringing, education, their religious ideology, etc.

Alternatively, studies focus on terrorist networks, which misses the role of an individual or relationships between individuals. However, human behaviour is constantly changing and those intent on causing harm, adapt and learn from the actions of others. This adaptation of behaviour makes prediction based on rational analysis more difficult and knowledge less useful.

In 2017 a bomb was detonated in an Ariana Grande concert in Manchester, England, killing 23 adults and children including the bomber. Despite the knowledge available, authorities had focused on what they anticipated were more immediate concerns and predictions had not been made. “The terrorist had been recognised as a potential danger, but he wasn’t high on the risk list” (Philip Williams, 2017). This is an example of the illusion of knowledge, i.e. when people with knowledge are not open to prediction.
Although prediction of terrorism is difficult, it is also not impossible.
The increase in data makes analysis and interpretation by humans more difficult and predictions therefore less likely to be accurate. However, increased knowledge from the study of real world data is being used in predictive modelling in machine learning. Multi-disciplined teams of psychologists, criminologists and others, such as mathematics that can build algorithms, are being applied to behavioural sciences. A combination of reasoning with intuition will increase knowledge and the ability to predict human behaviour.
In 2018 Israel claimed that one of its intelligence units provided information to Australian police, who foiled a plot in July 2017 to blow up a plane bound from Sydney to Abu Dhabi.

The Israeli unit used IT applications to collect and analyse data in order to obtain information. The police acted on that information, raiding several homes in Sydney. (The Guardian Australian Edition, 2018)

NSW Police Commissioner Mick Fuller quotes, “With terrorism you can’t wait, you can’t wait to put the whole puzzle together, you do have to go early because if you get it wrong the consequences are severe.” (ABC News, 2017) This statement further recognises the relationship between intuition and reasoning in predicting extreme human behaviour.

This threat in Sydney was planned by a group of people. Predicting and detecting group activity is less difficult than predicting the actions of an individual. “A terrorist group is fundamentally a social system” (Ze Li & Co, 2018). Studies have been made of terrorist groups and models have been built of the activity status of these groups. The predictive accuracy of each model is also being

measured.
However, an individual does not have the same need for social interaction, making it difficult to detect behavioural patterns that signal future intentions. “They decide to plan and execute ... entirely on their own, without participation from either a radical network or co-conspirators. That supercharges the problem of detection created by low-level terrorism more broadly, as police are even less likely to get advance warning about the attack” (Zack Beauchamp, 2017).
However, prediction of an individual is still possible and the UK has claimed to have disrupted five ‘individual’ attacks between March and June 2017.

Within the areas of both natural sciences and human sciences, it can be shown that there are linkages between prediction and knowledge. It is also clear that predictions have become more reliable because knowledge has expanded. This expansion is due to the increased availability of tools to gather diverse volumes of data in order to share information to teams from different disciplines, e.g. climatologists and meteorologists for improved weather predictions and psychologists and criminologists for improved predictions of criminal behaviour. Cross-functional teams bring different perspectives of reasoning and intuition to develop and improve predictions. Predictive reliability is further improved by adding mathematicians and other disciplines to analyse data. In conclusion, the statement made, “Those who have knowledge don’t predict. Those who predict don’t have knowledge” can be considered as too general. Predictions have improved with an increase in knowledge and will become more accurate, benefitting all of society.