Risk and Fear
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Political decisions are risky. They are often made in a context of uncertainty, time constraints, multiple pressures and economic restrictions. Decisions in (or involving) S&T routinely bring even more problems: lack of information, public fears, ethical and environmental questions. Advice to politicians should be technical and scientific, social and integrative.
In reality,
- How well do politicians understand the risks and uncertainties involved in new technologies?
- What about the science advisers?
- How much do public policies feed on public fears?
- And how much do these fears change the policies? (is this a chicken and egg problem?)
- How do public fears relate to the level of understanding of the technologies? Do we fear more what we don't understand?
A good example is the governments’ recent response to the H1N1 flu outbreak. There was serious discussion regarding how much of the scientific advice was based on certainties or on the role pharmaceutical lobbying groups might have played. Could an informed public have been more engaged in the discussion? Did the public fears precede or follow the public policies?
We try to answer some of these questions by developing novel techniques to help us identify patterns and trends in large datasets. We are using two different approaches:
1) By analyzing social networks and data from search engines we expect to extract patterns. We call this dataset “Emergent Perception” because it should be able to identify global trends that the individuals might not perceive. It can be compared to the implementations of different political decisions, or to the media coverage they received.
2) By surveying large groups of people, including scientists, policy-makers and the general public we propose to analyze their knowledge and levels of fear. We call this dataset “Rational Perception” because it should identify not only the actual knowledge, but also the conscious perceptions of risks. Naturally, both datasets can be compared to distinguish between unconscious trends and rational doubts.
Our complex system analysis can serve as a tool to analyze different policy decisions (and its consequences) in areas as diverse as nuclear energy, GMOs, pollution, etc.
Political decisions are risky. They are often made in a context of uncertainty, time constraints, multiple pressures and economic restrictions. Decisions in (or involving) S&T routinely bring even more problems: lack of information, public fears, ethical and environmental questions. Advice to politicians should be technical and scientific, social and integrative.
In reality,
- How well do politicians understand the risks and uncertainties involved in new technologies?
- What about the science advisers?
- How much do public policies feed on public fears?
- And how much do these fears change the policies? (is this a chicken and egg problem?)
- How do public fears relate to the level of understanding of the technologies? Do we fear more what we don't understand?
A good example is the governments’ recent response to the H1N1 flu outbreak. There was serious discussion regarding how much of the scientific advice was based on certainties or on the role pharmaceutical lobbying groups might have played. Could an informed public have been more engaged in the discussion? Did the public fears precede or follow the public policies?
We try to answer some of these questions by developing novel techniques to help us identify patterns and trends in large datasets. We are using two different approaches:
1) By analyzing social networks and data from search engines we expect to extract patterns. We call this dataset “Emergent Perception” because it should be able to identify global trends that the individuals might not perceive. It can be compared to the implementations of different political decisions, or to the media coverage they received.
2) By surveying large groups of people, including scientists, policy-makers and the general public we propose to analyze their knowledge and levels of fear. We call this dataset “Rational Perception” because it should identify not only the actual knowledge, but also the conscious perceptions of risks. Naturally, both datasets can be compared to distinguish between unconscious trends and rational doubts.
Our complex system analysis can serve as a tool to analyze different policy decisions (and its consequences) in areas as diverse as nuclear energy, GMOs, pollution, etc.
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