Virtual Reality Simulations of Wild Fire Prevention policies

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Virtual Reality Simulations of Wild Fire
Prevention policies
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Virtual Field Experiments
The University of Central Florida

• Virtual Experiments and Environmental Policy
• An Application to Valuation of Wild Fire
  Prevention
• Stephen M. Fiore, Glenn W. Harrison
• Charles E. Hughes, E. Elisabet Rutström

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Empirical Approaches

• Field data little control, many unobservables
• Lab Experiments high control, fewer
  un-observables
• Field Experiments using artefactual tasks
• Field Experiments using natural tasks
• Natural Experiments control, observability,
  natural task, natural environment
• Virtual Experiments more control,
  observability, natural task, natural environment

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Virtual Experiment

• Power of laboratory control and cognitive
  relevance of field task
• Representation of field task using Virtual
  Reality computer rendering
• Replicability larger sample
• Sample selection control - not restricted to
  participants who self select into environment

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Policy Question Valuation of policies to reduce
risk of wild fires

• Florida has 5,000 wild fires each year
• Wild fire season is March June
• 1998 were unusually severe
• Following a climate pattern of El Nino La Nina
  similar to this year
• 340 homes at 59,000
• 500 million acres of timber land at 1,000 per
  acre
• Health cost (asthma)
• Traffic accidents due to smoke
• Lost tourism revenues
• Total 1998 estimated cost 448 million
• Compare to 2006-7 beach restorations due to
  hurricane damage at 80 million
• State budget about 65 billion

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Wild Fire Prevention

• Mechanical Clearing
• Chemical Treatment
• Prescribed burn
• Latter is lower cost and better at preserving
  species habitats
• Stigma risk of causing wild fires, displeasing
  aesthetic effects
• 25 per acre
• ½ million acres treated each year in Florida
• 4 of all forested acreage

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Experiment

• Like Lottery choice but with realistic setting
• Choice between a riskier and a safer option
• Riskier option no additional fire prevention
• Safer costly prescribed burns as prevention
• Presentation of task
• Virtual rendering of both options
• Visual, dynamic, stochastic
• Simulation of fire spread using scientifically
  consistent model - Farsite

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Standard lottery choice Multiple Price List
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Pilot Experiment

• Choice between
• costly expansion of prescribed burn as fire
  prevention
• no expansion
• Ashley National Forest in Utah
• GIS data on topography and vegetation / fire fuel
• Stochastic environment
• Weather, wind, fuel moisture, lightning location
• 96 possible fire scenarios
• 48 for each of the prescribed burn choices
• High or low fuel loads

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Multiple Price List
Cost in Prescribed Burn Prescribed Burn
2 Yes No
4 Yes No
6 Yes No
8 Yes No
10 Yes No
12 Yes No
14 Yes No
16 Yes No
18 Yes No
20 Yes No
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Pilot Experiment Task

• Assigned a house as their own
• Given initial credit of 80
• Damage to house by simulated fire is -59
• Cost of prescribed burn (0 - 20)

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Risk to private house?

• Subjective risk perception formed through
  information on total acreage burned
• And through experience in 4 scenarios
• High fuel load and severe weather
• 65 of Ashley burns including house
• High fuel load and benign weather
• 9.5 of Ashley burns but not house
• Low fuel load and severe weather
• 9 of Ashley burns including house
• Low fuel load and benign weather
• 1 of Ashley burns but not house
• These scenarios are explored actively, not
  passively
• Moving in 3D plus time

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Procedures

• Demographic Questionnaire
• Training and paid test in navigation of VR
  environment
• Written instructions about wild fires, the
  simulations, and their task
• Experience in the 4 scenarios
• Choice of prevention Yes or No and if yes at
  what cost?
• 10 sided dice
• Implement prevention choice as low or high fuel
  load
• Random determination of weather, wind, fuel
  moisture, duration, and ignition location using
  dice
• Did house burn?
• Presence questionnaire
• Questionnaire on previous game and fire
  experiences
• Holt and Laury style risk attitude elicitation in
  independent task

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Pilot Data

• 12 subjects
• 5 males, 7 females
• Ages 18 57
• 6 had played video games before (all of the males
  plus the youngest female)
• Average WTP for prescribed burn 12 - 14 (row
  6-7)
• Average number of safe lottery choices in HL
  lottery 4.5 (CRRA approx 0.4)

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True probabilities and RN prediction
Risk averse subjects would switch on even higher
row
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Proportion of acreage burnt as estimate for
probability
Risk averse subjects would switch on even higher
row
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Actual choices

• Modal PB choice is row 6 (WTP12)
• Consistent with true probability of house burning
  and Risk Neutrality
• BUT 9 of the subjects are somewhat risk averse
• WTP of 12 plus risk aversion jointly implies
  that the perceived probability of house burning
  is less than the true one
• Closer to the probability calculated as the
  proportion of acreage burnt

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How good is VR?

• What is the distinction between a synthetic world
  and the actual world?
• Our experiences of the actual world are to a
  large extent synthetic
• Sensorial updating is incomplete
• The mind simulates the blanks
• Rendering of synthetic worlds can exploit this
  functioning of the mind
• VR technologies differ in how immersive they are
• Temporal coordination of sensory experiences and
  actions/reactions more important than graphical
  quality

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Presence?

• A sense of being there
• Sensory inputs dominated by stimulant
• Out of the 6 that had no video game experience, 5
  never finished the navigation test
• Presence questionnaire scored 1-7, we find
  average 5.1 5.7 across the 4 components
• Slightly higher than literature
• 3 of the 5 who failed navigation test scored
  lower on 3 of the components than did the other
  subjects

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Four factors

• Involvement
• How completely were you able to actively survey
  or search the environment using vision?
• How involved were you in the virtual environment
  experience?
• Adaptation/Immersion
• Were you able to anticipate what would happen
  next in response to the actions that you
  performed?
• How quickly did you adjust to the virtual
  environment experience?
• How completely were your senses engaged in this
  experience?
• Sensory Fidelity
• How closely were you able to examine objects?
• Interface Quality
• How much delay did you experience between your
  actions and expected outcomes?
• How much did the visual display quality interfere
  or distract you from performing assigned tasks or
  required activities?

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Conclusions from Pilot

• VX shows potential as an experimental
  environment that can provide field cues in
  controlled contexts
• By controlling for risk attitudes one can infer
  risk perceptions (latent beliefs)
• Or by inducing perceptions one can estimate risk
  attitudes in the presence of field cues

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Broader Questions

• Will responses differ from those using standard
  Contingent Valuation Methods?
• Text and pictures
• Hypothetical consequence to decisions
• Will responses of lay people and experts be more
  similar in VR setting than in CVM setting?
• Can simulation experiences serve as substitute
  for field experience?