globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2822
论文题名:
Rare disaster information can increase risk-taking
作者: Ben R. Newell
刊名: Nature Climate Change
ISSN: 1758-740X
EISSN: 1758-6860
出版年: 2015-10-05
卷: Volume:6, 页码:Pages:158;161 (2016)
语种: 英语
英文关键词: Climate-change impacts ; Psychology ; Decision making ; Communication
英文摘要:

The recent increase in the frequency and impact of natural disasters1 highlights the need to provide the public with accurate information concerning disaster prevalence. Most approaches to this problem assume that providing summaries of the nature and scale of disasters will lead people to reduce their exposure to risk2. Here we present experimental evidence that such ex post ‘news reports of disaster occurrences can increase the tolerance for risk-taking (which implies that rare events are underweighted3). This result is robust across several hundred rounds of choices in a simulated microworld, persists even when the long-run expected value of risky choices is substantially lower than safe choices, and is contingent on providing risk information about disasters that have been (personally) experienced and those that have been avoided (‘forgone outcomes). The results suggest that augmenting personal experience with information summaries of the number of adverse events (for example, storms, floods) in different regions may, paradoxically, increase the appeal of a disaster-prone region. This finding implies a need to communicate long-term trends in severe climatic events, thereby reinforcing the accumulation of events, and the increase in their associated risks, across time4.

For the past 20 years Munich Re have surveyed the previous years natural catastrophes. Their most recent report1 states: ‘It is not just that the number of natural catastrophes studied over the decades has increased … as a result of climate change, but that the impact of these events (as anticipated) has also become much greater and more costly. Thus, although climate change is gradual, its impact on communities is not only incremental and chronic but can also be sudden and acute because climate change alters the prevalence and severity of discrete climate-related negative events (for example, storms, floods, crop failures)5. This fact underscores the importance of understanding how people react to information about the risk of natural catastrophes.

A common response to this communication problem is to assume that more information is better, and that providing descriptive summaries of risk levels will lead people to reduce their exposure to relevant risks. This approach is taken in many fields; examples include information about vehicle accidents in a given area6, 7, 8, the risk of forest fire9, flood risks10, and terrorist attacks (for example, the US Traveler Enrollment Program).

Although evaluations of the response to these systems are scarce8, 9, the hoped-for positive effect of summarized information does not always materialize. Several studies suggest that publicly available information summaries concerning catastrophic events sometimes have the paradoxical effect of decreasing overall risk estimates11, 12, 13. For example, in a study of residents living in an area close to, but unaffected by the 2011 Tohoku tsunami13, participants were presented with scenarios involving waves of varying heights and asked whether each requires an evacuation. Comparing responses made before and after the tsunami, these unaffected residents estimates of wave heights warranting an evacuation were higher after the disaster. This suggests an increased tolerance for risk following the provision of information about a disaster that one avoided.

In two experiments, we investigated the (causal) effect of providing ex post information about each individual rare negative event that occurred in a simulated microworld (Fig. 1). Our participants made a choice about where to ‘live for each one of 400 rounds. The ‘microworld contained three regions, each having a village with multiple dwellings: participants could earn points for choosing to live in a particular dwelling in one of the villages; but lost many points when a catastrophe hit their dwelling. Points won represent an experimental analogue of the ‘utility garnered from a profitable and peaceful life in ones chosen ‘home, whereas points lost represent the ‘disutility of a life impacted by a major disaster. Throughout the experiment, all participants were given an accurate description of the ex ante risks of catastrophe in each region (Fig. 1). However by varying the type of ex post round-by-round feedback about when and where catastrophes occurred (described below), we examined how the reporting of negative events affected participants choice between regions that carried different levels of risk (Table 1).

Figure 1: Screenshot of the GeoRisk Microworld used in the experiments.
Screenshot of the GeoRisk Microworld used in the experiments.

On each round, participants could choose to reside in one of three regions (villages). Descriptive summaries about pay-offs and risks were provided (top right panel). In the Own House condition, only damage to the current dwelling was presented (denoted by a colour-filled square and a loss of points). In the Local Village condition, damage to all houses in the village where the participant currently resided was summarized (this is shown in the screenshot—the pink area endured a catastrophe affecting 10% of dwellings). In the All Villages condition, affected houses in all villages were displayed.

Participants in Experiment 1 were drawn from the University of Essex (UK) participant pool (N = 90, 56 female, mean age = 24.8; s.d. = 5.98) and those in Experiment 2 from the University of New South Wales (Australia) participant pool (N = 90, 64 female, mean age = 19.4; s.d. = 2.63). Both samples comprised predominately university students. In the absence of a direct prediction about effect size, we based our sample size (n = 30 per between-subjects condition) on our previous work using similar experimental paradigms20. In each experiment, participants were assigned randomly to one of the three between-subjects conditions: feedback received about Own House, Local Village, or All Villages. At the start of the experiments participants read summary instructions that described the basic set-up of the task. Following this, a screen similar to Fig. 1 was shown and participants were free to choose which of three regions (and which house within a region) to ‘live in for the current round. Each region included 100 houses. The allocation of colours to regions, and the position of the risky and safe regions on the screen (for example, by the coast, on the mountain), was randomized for each new participant. Once the choice was made, one round of the simulation was run according to the specified probabilities of disaster, and the participant received feedback (as specified by their assigned condition) about the occurrence, or non-occurrence, of a catastrophic event (in this case an earthquake). Participants were then told how many points they had earned on that round, and how many points they had lost (if a disaster had occurred)—both values were displayed on screen. Participants were then asked to choose a dwelling for the next round. There was no restriction on movement to regions or houses within those regions, but a ‘moving cost was implemented which was proportional to the distance from the current location. This moving cost was relatively low, being less than the amount the participant could expect to gain each round if no catastrophe occurred, and was subtracted from any earnings on each round. Participants were able to see the moving cost associated with each dwelling before committing to a move. The experiments then proceeded in this manner until trial 201, on which an on-screen dialog box announced that the pay-offs associated with two of the regions had now changed. In Experiment 1 the change was from moderate (in which disasters incurred a loss of 541 points) to severe (a loss of 819 points; see Table 1) and in Experiment 2 the change was from severe to moderate. The remaining 200 trials were then completed and at the end of the experiment participants were paid according to the number of points they had accumulated at a rate of 1,000 points = 1GBP in Experiment 1 and 1,000 points = 1.10AUD in Experiment 2. (An analysis of the points earned in each condition is presented in the Supplementary Information —see Supplementary Table 2). All participants were debriefed concerning the aims of the experiment. The procedures in both experiments were reviewed and passed by relevant ethics panels in the two institutions. A manual describing how to operate and implement the GeoRisk Microworld software used in the experiments is available at http://tx.technion.ac.il/˜yeldad/GM.

  1. Munich RE Topics GEO: Natural Catastrophes 2014 Analyses, Assessments, Positions 2015 Issue (Münchener Rückversicherungs-Gesellschaft, 2015); http://www.munichre.com/site/corporate/get/documents_E1018449711/mr/ assetpool.shared/Documents/5_Touch/_Publications/302-08606_en.pdf
  2. Kunreuther, H. et al. Risk management and climate change. Nature Clim. Change 3, 447450 (2013).
  3. Erev, I. & Roth, A. Maximization, learning and economic behaviour. Proc. Natl Acad. Sci. USA 111, 1081810825 (2014).
  4. Weber, E. U. Experience-based and description-based perceptions of long-term risk: Why global warming does not scare us (yet). Clim. Change 77, 103120 (2006).
  5. Trenberth, K. E. Framing the way to relate climate extremes to climate change. Clim. Change 115, 283290 (2012).
  6. Miller, J. S. Geographical information systems: Unique analytic capabilities for the traffic safety community. Trans. Res. Rec. 1734, 2128 (2000).
  7. Plug, C., Xia, J. & Caulfield, C. Spatial and temporal visualisation techniques for crash analysis. Accid. Anal. Prev. 43, 19371946 (2011).
  8. Zheng, Y. A preliminary evaluation of the impact of local accident information on the public perception of road safety. Reliab. Eng. Sys. Saf. 92, 11701182 (2007).
  9. Donovan, G. H., Champ, P. A. & Butry, D. T. Wildfire risk and housing prices: A case study from Colorado Springs. Land Econ. 83, 217233 (2007).
  10. Samarasinghe, O. & Sharp, B. Flood prone risk and amenity values: A spatial hedonic analysis. Aust. J. Agric. Resour. Econ. 54, 457475 (2010).
  11. Beron, K., Murdoch, J., Thayer, M. & Vijverberg, W. An analysis of the housing market before and after the 1989 Loma Prieta earthquake. Land Econ. 73, 101113 (1997).
  12. Palm, R. I. Public response to earthquake hazard information. Ann. Assoc. Am. Geogr. 71, 389399 (1981).
  13. Oki, S. & URL:
http://www.nature.com/nclimate/journal/v6/n2/full/nclimate2822.html
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/4568
Appears in Collections:气候变化事实与影响
科学计划与规划
气候变化与战略

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Ben R. Newell. Rare disaster information can increase risk-taking[J]. Nature Climate Change,2015-10-05,Volume:6:Pages:158;161 (2016).
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