1. Understanding Risk

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 assessment, emphasizing its importance. The Royal Society of Britain published a white book on risk assessment highlighting the necessity to understand what risk means. It raises questions about public belief in risks and the credibility of assessments.

Imagine a weather forecast warning about a high chance of flooding. Even though scientists base their predictions on data, some people may dismiss it, believing it won't happen to them. This reflects a common skepticism about risk assessments.

They actually asking many famous acknowledged internationally acclaimed professors, scientists to estimate and tell them what is risky, to talk about a risky but very funny thing is that when these society is publishing this white paper, they are saying then you know disclaimer they are not saying that this report is not a report of the society, the views expressed are those of the authors alone.

This chunk discusses the involvement of experts in risk assessment. However, it points out that despite the input from well-known scientists and professors, the society distances itself from the findings by stating the views are solely those of the authors. This disclaimer indicates the inherent uncertainties in risk assessment.

Think of a movie review where a critic says, 'This is purely my personal opinion.' While it may provide insights, it doesn’t guarantee viewers will agree or react the same way, demonstrating the variability in individual perspective.

Experts and scientists are called and but disagreement continued about risk. So, actual risk we as scientists saying that we there is actually an actual risk, what is that? 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.

This chunk differentiates between two types of risks: objective risk and perceived risk. Objective risk is based on scientific data and analysis, whereas perceived risk reflects how individuals interpret and anticipate risks based on their experiences and beliefs.

For instance, consider a person who is afraid of flying but feels completely safe driving a car. Statistically, flying is often safer than driving. Here, the objective risk of flying is low, but the perceived risk is high due to the individual's fears.

Now, risk in general, we know the probability of a particular adverse event to occur during a particular period of time.

This chunk explains risk as a probability concept, highlighting that risk involves calculating the likelihood of specific adverse events happening over time. This understanding helps in evaluating potential dangers and preparing accordingly.

It's like predicting your chances of getting rained on tomorrow. If weather predictions say there's a 70% chance of rain, you can make an informed decision about whether to take an umbrella or not.

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.

This chunk discusses how risks are assessed using numerical measures. Risk can often be quantified as financial losses, like in cases of natural disasters. This provides a tangible way to understand and communicate the impact of various risks.

When a company estimates the financial loss from a factory shutdown due to an earthquake, they may say it could cost them $5 million. This numerical quantification helps everyone understand the potential severity of the risk involved.

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 emphasizes the critical role of data in risk assessment. The more accurate data available, the better scientists can refine risk estimates and provide informed decisions about risk management.

Consider a doctor diagnosing a patient. The more medical history, test results, and other data they have, the more accurately they can assess the patient's health risks.

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 illustrates the gap between scientific understanding of risk and personal perception. Despite the known dangers of smoking, some individuals might continue due to personal beliefs or social influences, highlighting discord between informed risk and individual behavior.

Imagine someone who knows that eating junk food is unhealthy yet continues to indulge because they enjoy it. This demonstrates how personal enjoyment can overshadow scientific evidence regarding risk.

Let us look, reducing the gap; they are saying that it is very important for the risk manager that what scientifically true, and what people think we should reduce that gap.

This chunk states the importance of bridging the gap between scientific risk assessments and public perception. Risk managers are encouraged to communicate effectively about scientific truths to help the public understand real risks better.

Think of fire safety drills in schools. Educators explain the importance of evacuation procedures based on statistics showing how drills save lives. This understanding can help students take practices more seriously, reducing the perception gap.

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