Zero-risk bias

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Zero-risk bias is a tendency to prefer the complete elimination of risk in a sub-part over alternatives with greater overall risk reduction. [1] It often manifests in cases where decision makers address problems concerning health, safety, and the environment. [2] Its effect on decision making has been observed in surveys presenting hypothetical scenarios. [3]

Contents

Explanation

Zero-risk bias is based on the way people feel better if a risk is eliminated instead of being merely mitigated. [2] Scientists identified a zero-risk bias in responses to a questionnaire about a hypothetical cleanup scenario involving two hazardous sites X and Y, with X causing 8 cases of cancer annually and Y causing 4 cases annually. The respondents ranked three cleanup approaches: two options each reduced the total number of cancer cases by 6, while the third reduced the number by 5 and eliminated the cases at site Y. While the latter option featured the worst reduction overall, 42% of the respondents ranked it better than at least one of the other options. This conclusion resembled one from an earlier economics study that found people were willing to pay high costs to eliminate a risk. [4] [5] It has a normative justification since once risk is eliminated, people would have less to worry about and such removal of worry also has utility. [6] It is also driven by our preference for winning much more than losing as well as the old instead of the new way, all of which cloud the way the world is viewed. [7]

Multiple real-world policies have been said to be affected by this bias. In American federal policy, the Delaney clause outlawing cancer-causing additives from foods (regardless of actual risk) and the desire for perfect cleanup of Superfund sites have been alleged to be overly focused on complete elimination. Furthermore, the effort needed to implement zero-risk laws grew as technological advances enabled the detection of smaller quantities of hazardous substances. Limited resources were increasingly being devoted to low-risk issues. [8]

Critics of the zero-risk bias model cite that it has the tendency to neglect overall risk reduction. For instance, when eliminating two side effects, it holds that the complete eradication of just one side-effect is preferable to lowering the overall risk. [9]

Causes

Other biases might underlie the zero-risk bias. One is a tendency to think in terms of proportions rather than differences. A greater reduction in proportion of deaths is valued higher than a greater reduction in actual deaths. The zero-risk bias could then be seen as the extreme end of a broad bias about quantities as applied to risk. Framing effects can enhance the bias, for example, by emphasizing a large proportion in a small set, or can attempt to mitigate the bias by emphasizing total quantities. [10]

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Statistical bias, in the mathematical field of statistics, is a systematic tendency in which the methods used to gather data and generate statistics present an inaccurate, skewed or biased depiction of reality. Statistical bias exists in numerous stages of the data collection and analysis process, including: the source of the data, the methods used to collect the data, the estimator chosen, and the methods used to analyze the data. Data analysts can take various measures at each stage of the process to reduce the impact of statistical bias in their work. Understanding the source of statistical bias can help to assess whether the observed results are close to actuality. Issues of statistical bias has been argued to be closely linked to issues of statistical validity.

<span class="mw-page-title-main">Exponential growth</span> Growth of quantities at rate proportional to the current amount

Exponential growth is a process that increases quantity over time at an ever-increasing rate. It occurs when the instantaneous rate of change of a quantity with respect to time is proportional to the quantity itself. Described as a function, a quantity undergoing exponential growth is an exponential function of time, that is, the variable representing time is the exponent. Exponential growth is the inverse of logarithmic growth.

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<span class="mw-page-title-main">Superfund</span> US federal program to investigate / clean up sites contaminated with hazardous substances

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The availability heuristic, also known as availability bias, is a mental shortcut that relies on immediate examples that come to a given person's mind when evaluating a specific topic, concept, method, or decision. This heuristic, operating on the notion that, if something can be recalled, it must be important, or at least more important than alternative solutions not as readily recalled, is inherently biased toward recently acquired information.

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Status quo bias is an emotional bias; a preference for the maintenance of one's current or previous state of affairs, or a preference to not undertake any action to change this current or previous state. The current baseline is taken as a reference point, and any change from that baseline is perceived as a loss or gain. Corresponding to different alternatives, this current baseline or default option is perceived and evaluated by individuals as a positive.

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In social science research, social-desirability bias is a type of response bias that is the tendency of survey respondents to answer questions in a manner that will be viewed favorably by others. It can take the form of over-reporting "good behavior" or under-reporting "bad", or undesirable behavior. The tendency poses a serious problem with conducting research with self-reports. This bias interferes with the interpretation of average tendencies as well as individual differences.

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The neglect of probability, a type of cognitive bias, is the tendency to disregard probability when making a decision under uncertainty and is one simple way in which people regularly violate the normative rules for decision making. Small risks are typically either neglected entirely or hugely overrated. The continuum between the extremes is ignored. The term probability neglect was coined by Cass Sunstein.

Omission bias is the phenomenon in which people prefer omission (inaction) over commission (action) and people tend to judge harm as a result of commission more negatively than harm as a result of omission. It can occur due to a number of processes, including psychological inertia, the perception of transaction costs, and the perception that commissions are more causal than omissions. In social political terms the Universal Declaration of Human Rights establishes how basic human rights are to be assessed in article 2, as "without distinction of any kind, such as race, colour, sex, language, religion, political or other opinion, national or social origin, property, birth or other status." criteria that are often subject to one or another form of omission bias. It is controversial as to whether omission bias is a cognitive bias or is often rational. The bias is often showcased through the trolley problem and has also been described as an explanation for the endowment effect and status quo bias.

The ambiguity effect is a cognitive tendency where decision making is affected by a lack of information, or "ambiguity". The effect implies that people tend to select options for which the probability of a favorable outcome is known, over an option for which the probability of a favorable outcome is unknown. The effect was first described by Daniel Ellsberg in 1961.

Acquiescence bias, also known as agreement bias, is a category of response bias common to survey research in which respondents have a tendency to select a positive response option or indicate a positive connotation disproportionately more frequently. Respondents do so without considering the content of the question or their 'true' preference. Acquiescence is sometimes referred to as "yea-saying" and is the tendency of a respondent to agree with a statement when in doubt. Questions affected by acquiescence bias take the following format: a stimulus in the form of a statement is presented, followed by 'agree/disagree,' 'yes/no' or 'true/false' response options. For example, a respondent might be presented with the statement "gardening makes me feel happy," and would then be expected to select either 'agree' or 'disagree.' Such question formats are favoured by both survey designers and respondents because they are straightforward to produce and respond to. The bias is particularly prevalent in the case of surveys or questionnaires that employ truisms as the stimuli, such as: "It is better to give than to receive" or "Never a lender nor a borrower be". Acquiescence bias can introduce systematic errors that affect the validity of research by confounding attitudes and behaviours with the general tendency to agree, which can result in misguided inference. Research suggests that the proportion of respondents who carry out this behaviour is between 10% and 20%.

<span class="mw-page-title-main">Bias–variance tradeoff</span> Property of a model

In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number of tunable parameters in a model, it becomes more flexible, and can better fit a training data set. It is said to have lower error, or bias. However, for more flexible models, there will tend to be greater variance to the model fit each time we take a set of samples to create a new training data set. It is said that there is greater variance in the model's estimated parameters.

Risk aversion is a preference for a sure outcome over a gamble with higher or equal expected value. Conversely, rejection of a sure thing in favor of a gamble of lower or equal expected value is known as risk-seeking behavior.

References

  1. ↑ Crosby, Daniel (2016-06-27). The Laws of Wealth: Psychology and the secret to investing success. Harriman House Limited. ISBN   9780857195258.
  2. 1 2 Virine, Lev; Trumper, Michael (2016). ProjectThink: Why Good Managers Make Poor Project Choices. Oxon: Routledge. pp. 102, 175. ISBN   9781409454984.
  3. ↑ Wells, Andrew Roman; Chiang, Kathy (2017). Monetizing Your Data: A Guide to Turning Data into Profit-Driving Strategies and Solutions. Hoboken, NJ: John Wiley & Sons. p. 143. ISBN   9781119356240.
  4. ↑ Baron, Jonathan; Gowda, Rajeev; Kunreuther, Howard (1993). "Attitudes toward managing hazardous waste: What should be cleaned up and who should pay for it?". Risk Analysis. 13 (2): 183–192. doi:10.1111/j.1539-6924.1993.tb01068.x.
  5. ↑ Viscusi, W. K.; Magat, W. A.; Huber, J. (1987). "An investigation of the rationality of consumer valuation of multiple health risks". RAND Journal of Economics. 18 (4): 465–479. doi:10.2307/2555636. hdl: 1803/6944 . JSTOR   2555636.
  6. ↑ Baron, Jonathan (2003). Thinking and Deciding. Cambridge: Cambridge University Press. p. 506. ISBN   978-0521659727.
  7. ↑ Crosby, Daniel (2016-06-27). The Laws of Wealth: Psychology and the secret to investing success. Hampshire, UK: Harriman House Limited. ISBN   9780857195258.
  8. ↑ Kunreuther, Howard (1991). "Managing hazardous waste: past, present and future" (PDF). Risk Analysis. 11: 19–26. doi:10.1111/j.1539-6924.1991.tb00561.x.
  9. ↑ Raue, Martina; Streicher, Bernhard; Lermer, Eva (2019). Perceived Safety: A Multidisciplinary Perspective. Cham, Switzerland: Springer. p. 65. ISBN   9783030114541.
  10. ↑ Baron, Jonathan (2003). "Value analysis of political behavior - self-interested : moralistic :: altruistic : moral". University of Pennsylvania Law Review. 151 (3): 1135–1167. doi:10.2307/3312887. JSTOR   3312887.
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