Modelling evolving patterns of land use: The FEARLUS model

1
Modelling evolving patterns of land useThe
FEARLUS model
ESRC Seminar SeriesMicrosimulation modelling in
the UK bridging the gapsSeminar 2 Adding
behaviour

• Gary Polhill and Nick Gotts
• Macaulay Land Use Research Institute

2
Acknowledgements

• Various collaborators
• Alistair Law, Dawn Parker, Luis
  Izquierdo,Lee-Ann Sutherland, Pernette Belveze,
  Pete Edwards,Alun Preece, Edoardo Pignotti,
  Alessandro Gimona
• Funding
• Scottish Government Rural and Environmental
  Research and Analysis Directorate
• ESRC National Centre for eSocial Science
• EU FP6 New and Emerging Science and Technology
  Pathfinder Initiative on Tackling Complexity in
  Science

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Work with FEARLUS

• The (two) original FEARLUS projects
• Building and developing the FEARLUS model
• FEARLUS-CAMEL
• Linking land use change to diffuse pollution
• FEARLUS-SPOMM
• Linking land use change to biodiversity
• FEARLUS-ELMM
• Improving the land market model
• CAVES (FP6)
• Using qualitative research to enhance and
  validate FEARLUS
• Exploring complex dynamics in FEARLUS
• Other work not (directly) relevant to land use
• ESRC funded work on enabling ABMs on the semantic
  grid
• Managing outputs from ABMs and making explicit
  experimental procedure
• Issues of numerics in ABMs
• Behaviour of ABMs can be influenced by improperly
  handled floating-point exceptions

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Outline

• Inspiration and evidence for FEARLUS
• Heuristic decision making in FEARLUS
• Early work with FEARLUS
• More sophisticated decision-making
• Adding Case-Based Reasoning
• Other areas of decision-making and concluding
  points

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Outline

• Inspiration and evidence for FEARLUS
• Heuristic decision making in FEARLUS
• More sophisticated decision-making
• Other areas of decision-making and concluding
  points

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Predicting forestry in Scotland
(Aspinall Birnie, unpub.)

• Forestry is profitable
• Probability of forestry
• Dark green higher P
• Based on suitability
• Climate
• Gradient
• Soil type

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Predicted versus actual forestry

• Yellow shows actual forestry
• Green shows predictions
• Large areas of high suitability but no forestry

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Influence of land ownership

• Red lines show ownership boundaries
• Land use is based on more than suitability and
  (simple) economics
• Sociological factors
• e.g. Grouse shooting
• Landscape pattern at the regional scale is a
  function of local interactions and individual
  preferences

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Anecdotal evidence

• The Guardian, UK, Tuesday 24 April 2007
• Kemble Farms has been getting 19p per litre for
  milka loss of 2p per litre
• The irony for Colin Rank, one of the family
  that owns Kemble Farms, is that his cows drink
  water from a Cotswold spring that he could bottle
  and sell for 80p a litre. Were giving it to
  cows and devaluing it by turning it into milk.
  Like all dairy farmers we could pack up tomorrow
  and do something better with our capital, but we
  do it because we have an emotional investment in
  the land and the animals. And we know theres a
  market for our product, if only the market
  worked.
• Felicity Lawrence

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Decision-making in rural systems

• Land manager decision making isnt (entirely)
  fiscally rational
• Multidimensional
• Desire to be seen by peers as a good farmer
  Burton, 2004
• Keeping the name on the farm identity Burton
  and Wilson, 2006
• Conservation
• Uncertainty means utility maximisation is not
  appropriate
• Satisficing, heuristic strategies popular in
  models Parker et al. 2008
• Can also use algorithms grounded in cognitive
  theory
• Interactive decision-making
• Social influences imitation, advice, approval,
• If it were, then competition with less regulated
  global agricultural systems could mean bad news
  for
• Soil, water, biodiversity, wildlife, animal
  welfare, landscape amenity, workers rights, food
  security, disease control,

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Outline

• Inspiration and evidence for FEARLUS
• Heuristic decision making in FEARLUS
• More sophisticated decision-making
• Other areas of decision-making and concluding
  points

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Original model
Calculation of economic return from each land
parcel
Before
Land sales
After
Externalconditions
Yearly Cycle
Calculationof Return
Land use
Biophysical properties
Biophysical properties
Externalconditions(last n years)
Returns and biophysical properties from other
land parcels belonging to self and
neighbours(last n years)
Returns(last n years)
Land use selection
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FEARLUS Agents

• Decision algorithm chooses land uses
• Wealth
• No theoretical limit to age provided that wealth
  gt 0
• Forced to buy land parcels if sufficient
  wealthForced to sell land parcels if wealth lt 0

• Social neighbourhood

(von Neumann neighbourhood)
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Decision algorithm
Habit
Satisfied with yield?
Yes
Yield Copying
Majority Copying
No
Optimum Copying
Copy neighbours?
Yes
Random
No
Parcel Matching
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Experimentation and Imitation
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Some of the sub-populations

• Sub-population SI
• Always use Majority Copying strategy
• Imitate based on a weighted selection of the
  number of times Land Uses appear in the
  neighbourhood
• Sub-population II
• Always use Optimum Copying strategy
• Choose the best performing Land Use in the
  neighbourhood, with assessment based on a
  knowledge of differences in Biophysical
  Characteristics between neighbouring Parcels and
  that for which a Land Use is being chosen
• Sub-population HRYI
• Use Habit strategy if yield of parcel gt 11
• Else use Random strategy with probability 1/16
• Else use Yield Copying strategy
• Imitate based on a weighted selection of the
  total Yield each Land Use has in the
  neighbourhood

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Comparing decision algorithms

• 30 trials containing two sub-populations
• Each sub-population contains agents with a
  particular decision algorithm
• The winning sub-population in each trial is
  that owning the majority of the land parcels
  after year 200
• Binomial test used to see if one sub-population
  beats the other a significant number of times

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Non-transitivity of winners
II
Why didnt II beat SI?
HRYI
SI
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SI vs HRYI

• Varying dominance of land use
• SI slow on the uptake waits for new land uses to
  become established
• HRYI (SubPop 2) beats SI

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II vs HRYI

• Dominant land use changes over time with
  fluctuations in climate/market
• Steeper adoption curve
• II is able to exploit risk taking strategy of
  HRYI
• II (SubPop 1) ends up with more land parcels

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II vs SI

• Lock-in on land use 3
• SI and II both have purely imitative decision
  algorithms
• Neither SI nor II is able to dominate

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Wider questions on imitation

• When to copy?
• When aspiration threshold breached?
• At what level should the aspiration be set?
• Gotts and Polhill, 2003
• There are various ways that imitation could be
  implemented
• Copy the highest yield in the neighbourhood
• Copy the most used in the neighbourhood
• (Compromise) Copy by total yield
• Weighted selection from the neighbourhood
• Forthcoming paper in JASSS

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FEARLUS and microsimulation

• We have used some microsimulation-like studies to
  examine simple models
• Why using an aspiration threshold (satisficing)
  is an advantage
• Why diversifying land use selections can be an
  advantage in highly unpredictable environments
• Use decision-making algorithms that dont involve
  interactions with neighbours
• Well suited to simple heuristic algorithms
• Use only a small number of land parcels

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Possible Land Parcel Histories in Simplified
Model Random Choice vs Solvency Threshold HR
Managers
Year Zero
Year Zero
Random Choice Manager Yield above Solvency
Threshold
Solvency Threshold HR Manager Yield above
Solvency Threshold
Solvency Threshold HR Manager Yield
above Solvency Threshold
Solvency Threshold HR Manager Yield
below Solvency Threshold
Solvency Threshold HR Manager Yield below
Solvency Threshold
Random Choice Manager Yield below Solvency
Threshold
Year Zero
Year Zero
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The Advantages of Diversity when all Land Uses
are equalWithin-Estate Diversity in a
Two-Parcel Environment
Symmetrical Random Walk model

• Both Parcels the same.
• Two possible Land Uses, Yields of BET1/2 and
  BET-1/2.
• Equal probability each will be the good Land
  Use in any Year.
• LPP 1.

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Outline

• Inspiration and evidence for FEARLUS
• Heuristic decision making in FEARLUS
• More sophisticated decision-making
• Other areas of decision-making and concluding
  points

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FEARLUS Diffuse pollution

• Diffuse agricultural pollution generally refers
  to runoff from fields
• Nitrates, Phosphates, Pesticides, Faecal
  coliforms
• Could also be applied to airborne pollutants
• Pesticides, Greenhouse gases (methane, nitrous
  oxide)
• Pollutants in runoff to rivers can be monitored
  downstream
• Increasing ability to monitor airborne
  pollutants, but not at single-farm scale
• Monitoring is less costly (in some cases only
  possible) at above farm scale e.g. over a
  catchment or sub-catchment
• Could social interactions between farmers be used
  to make such monitoring effective in reducing
  pollution?

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Farmers and their Neighbours

• Farmers both compete and cooperate with peers and
  neighbours
• Farmers learn from their peers and neighbours
• Farmers are not straightforward
  profit-maximisers they value the good opinion of
  peers and neighbours
• In itself to be seen as a good farmer
• Because they may need neighbours help
• Refocusing Under what circumstances would
  collectively-earned payment for pollution
  reduction be a policy instrument worth
  considering?

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FEARLUS-W
Land use selection
Calculation of Return
Climate
Land Uses
Estimated Yield
Market Conditions
Land use
Biophysical properties
Yearly Cycle
Estimated Social Acceptability
Pollution
Return
Before
Neighbours Approval/Disapproval
After
Social Interactions
Land sales
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FEARLUS-WModel of Farmer Decision-Making

• Profit, Social Approval and Salience
• FEARLUS-W Land Managers choose land uses on the
  basis of expected profit and expected approval
  from neighbours
• Relative importance of these varies between Land
  Managers and over time
• Change over time due to salience-changing events,
  e.g. a bad harvest, a neighbours disapproval
• Case-Based Reasoning
• Land Managers maintain an episodic memory, or
  case base
• Every Year they consider whether they are
  satisfied with how their neighbours assess them,
  and for each land parcel, whether it is yielding
  a satisfactory return.
• If satisfied, they do not change land use for
  that parcel.
• Otherwise, they consider past experiences with
  each land use, selecting the case most similar to
  the current case. Judgement of similarity is
  based on
• climatic conditions of the case compared to those
  expected in the coming year,
• economic conditions of the case compared to those
  expected in the coming year,
• proximity of the land parcel in the case to that
  now being considered.
• If no suitable case is found, default values are
  used.

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Basis for Case Based Reasoning(From Izquierdo
2008, PhD Thesis)

• Case-Based Reasoning arose from Cognitive Science
  in the late 1970s
• Knowledge gained from experience is encoded in
  episodic memory as scripts allowing us to set
  up expectations and inferences (Schank Abelson)
• Supported by psychological studies
• Klein and Calderwood (1988) conclude from a study
  of 400 decisions that processes involved in
  retrieving and comparing prior cases are far more
  important in naturalistic decision making than
  are the application of abstract principles, rules
  or conscious deliberation between alternatives

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Multidimensional decision making
Salience determines choice
Estimated Profit
Estimated Social Acceptability
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Description of FEARLUS Runs Performed

• Toroidal environment of 20 20 land parcels
• Spatially variable biophysical conditions and
  temporally variable (but auto-correlated)
  climatic conditions determining yield, temporally
  variable but auto-correlated economic conditions
  then determining economic return jointly with
  yield.
• Each Land Manager initially owning 1 parcel
  (unsuccessful Land Managers sell up)
• Five land uses, with mean yield varying linearly
  with pollution generated (both expressed in
  arbitrary units) thus

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Description of FEARLUS Runs Performed

• Six reward conditions
• Threshold 2000, reward per land parcel 50
• Threshold 2000, reward per land parcel 25
• Threshold 1750, reward per land parcel 50
• Threshold 1750, reward per land parcel 25
• Threshold 1500, reward per land parcel 50
• No reward
• Six neighbour-(dis)approval and (dis)approval
  salience-increasing conditions
• Base (dis)approval on absolute pollution levels,
  increase salience of neighbours opinion when
  disapproved of
• Base (dis)approval on absolute pollution levels,
  increase salience of neighbours opinion when
  reward not given
• Base (dis)approval on relative pollution levels,
  increase salience of neighbours opinion when
  disapproved of
• Base (dis)approval on relative pollution levels,
  increase salience of neighbours opinion when
  reward not given
• Base (dis)approval on relative pollution levels,
  disapprove more strongly than approve, increase
  salience of neighbours opinion when disapproved
  of
• No concern with neighbours, so no Social Approval
  Function

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Pollution
No Social Approval
With Social Approval
No Reward
No Reward
No Social Approval
With Social Approval
Reward 50 _at_ 2000
Reward 50 _at_ 2000
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Land Uses
No Social Approval
With Social Approval
No Reward
No Reward
No Social Approval
With Social Approval
Reward 50 _at_ 2000
Reward 50 _at_ 2000
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Decision Making Mode
No Social Approval
With Social Approval
No Reward
No Reward
No Social Approval
With Social Approval
Reward 50 _at_ 2000
Reward 50 _at_ 2000
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Some observations

• The larger the reward, the less time Land
  Managers spent using more polluting land uses
• Social approval lowered pollution
• if Land Managers cared about neighbours
  opinions and disapproved of neighbours polluting
• Taken together, the above were more effective
  than either acting separately
• The general pattern of a simulation run in which
  the reward had an effect was of variation in
  pollution levels as exogenous factors changed
  land uses profitability
• Levels much above the threshold were seldom
  maintained for long periods
• However, the algorithm used did make a difference
  to the size of the reduction effect

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Outline

• Inspiration and evidence for FEARLUS
• Heuristic decision making in FEARLUS
• More sophisticated decision-making
• Other areas of decision-making and concluding
  points

40
Other areas of decision-making in FEARLUS

• Advice model
• Allows agents to exchange cases when they dont
  have experience
• Used in recent work with Alessandro Gimona, on
  biodiversity
• Not yet adequately explored
• Land market decisions (with Dawn Parker)
• Using non-optimising decision making means
  various questions have to be answered
• When to sell? When to buy?
• What to sell? What to buy?
• What price to accept? What price to offer? What
  is the final sale price?
• Decisions about these affect outcomes in the model

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Concluding points

• Agent-Based Modelling does not constrain
  assumptions about decision-making to
• Optimisation/Maximisation
• (Fiscal) Rationality
• Non-interactive
• Decision-making in ABM can be based on cognitive
  theory
• Results can be affected by different algorithms
  used to implement behaviour
• In FEARLUS, we explore alternatives
• Assumption of optimisation/rationality/non-interac
  tivity is still an assumption
• Mathematical tractability is no longer an excuse
  for failing to look formally at other assumptions
  and algorithms