1.4. Risk Determinants

Here is a very important data, then what is risk? Why people are not believing risk? there is a Britain Royal Society; they publish a White book on risk assessment in 1982 and in 1983, it was revised again.

This chunk introduces the concept of risk and raises questions about its understanding and acceptance among people. The Royal Society's publication indicates an effort to consolidate expert opinions on risk assessment, showing that even recognized authorities have sought to clarify what constitutes risk.

Imagine a daily weather forecast where experts predict a storm. Some people dismiss the warning, thinking it won't really happen. Just like the Royal Society gathering experts to define risk, meteorologists use data and models to forecast weather, yet people often respond to risks based on personal beliefs.

So, I am not taking the responsibility here; society is telling, no collective view about risk, so it is not about to tell you that this is what is risk, a forum of debate.

This chunk discusses a disclaimer provided by the Royal Society regarding their findings on risk, emphasizing that the report reflects individual opinions rather than a unified stance. This illustrates the complexity and varied perceptions of risk even among experts, indicating that risk is a topic that remains under debate.

Think of a group of friends discussing which movie to watch. Each has a different opinion on what makes a movie enjoyable. Similarly, the Royal Society acknowledges that their findings on risk are just individual perspectives in an ongoing discussion.

So, we are saying that there are 2 kind of risk; one is objective risk that is scientific risk; another one is the perceived risk.

Here, the text introduces two specific categories of risk: objective risk and perceived risk. Objective risk is based on scientific measurements and calculations, while perceived risk is how individuals view or interpret risks based on personal experiences or biases. Understanding both types is crucial for effective risk management.

Consider driving a car. Objective risk includes statistical data about accidents, while perceived risk varies from person to person—some may feel safe, while others with anxiety may see it as dangerous, regardless of actual statistics.

So, determinant of risk; how we determine a risk? Generally, we determine any kind of risk by numerical measures, like expressed in chance of that much cost in dollar or in rupees, loss is expected to due to a flood, a loss of productivity has been lost, that much of amount due to earthquake so, these always we express in numerical figure; 5 billion, 20 billion, 200 billion, or, 50 people died, 100 people died like that.

This chunk emphasizes that risk is often quantified with numerical measures, which helps to express the likelihood and potential impacts of adverse events. These figures provide a clearer picture of what risks entail in practical terms, making it easier for decision-makers to understand and manage them.

Imagine you are considering buying flood insurance for your home. The insurance company might present statistics showing that 1 in 100 homes in your area flood every year, and they could quantify potential losses in tens of thousands of dollars. This numerical representation allows you to assess your risk and make informed decisions.

So, probability and magnitude often adverse event. Now, risk estimation progress; if we want to progress more if you want to refine our estimation, one thing is very clear that we need data, without data we cannot do it so, more data where you have, the more fine-tuned, more cutting-edge estimations we can make.

This chunk discusses the critical role of data in refining risk estimations. It highlights that better data leads to more accurate predictions and assessments of risk. Collecting extensive data allows scientists and managers to make informed decisions about risk mitigation strategies.

Think of cooking a new dish. If you only have vague instructions, the outcome may be inconsistent. However, if you have detailed recipes with measurements and ingredients, your cooking will be more successful. Similarly, accurate data leads to better risk assessments.

So, but they are making it very simple, the scientists are saying that you need data but remember that risk perception that is subjective risk, what laypeople think. Please do not incorporate that element in disaster risk management, is it really so?

In this chunk, the text addresses the difference between objective data-driven assessments of risk and subjective perceptions held by individuals. The scientists aim to create a clear distinction, suggesting that disaster risk management should primarily rely on data while questioning whether laypeople's perceptions should be ignored in practice.

Consider a community's fear of a new bridge being built. Engineers may assure everyone that it meets safety standards based on data, yet if local residents feel uncomfortable about it, their perceptions cannot be entirely overlooked. Effective communication about the safety based on data can help alleviate concerns.

For example here, if you are smoking you are at risk, you believe or not you may be doing it because you want to be macho, or your body needs nicotine, but once you were smoking you are at risk that is very clear.

This chunk provides an example of personal risk perception. It illustrates that regardless of an individual's beliefs, actions such as smoking carry established risks. The example emphasizes the contrast between objective risk (the actual danger of smoking) and perceived risk (how individuals interpret that danger).

Imagine a friend who thinks drinking energy drinks is harmless because they're marketed as safe. While they feel invulnerable, the reality is that excessive caffeine can lead to health issues. This illustrates how personal beliefs can overshadow the objective risks revealed by scientific studies.

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