Modelling evolving patterns of land use: The FEARLUS model

ESRC Seminar SeriesMicrosimulation modelling in the UK: bridging the gapsSeminar 2: ‘Adding behaviour’ Modelling evolving patterns of land use:The FEARLUS model Gary Polhill and Nick Gotts Macaulay Land Use Research Institute

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

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

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

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

Predicting forestry in Scotland (Aspinall & Birnie, unpub.) • Forestry is profitable • Probability of forestry • Dark green = higher P • Based on suitability • Climate • Gradient • Soil type

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

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

‘Anecdotal’ evidence • The Guardian, UK, Tuesday 24 April 2007 Kemble Farms has been getting 19p per litre for milk[a 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. “We’re 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 there’s a market for our product, if only the market worked.”’ [Felicity Lawrence]

Decision-making in rural systems • Land manager decision making isn’t (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, …

£ £ 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

Decision algorithm chooses land uses Wealth No theoretical limit to age provided that wealth >= 0 Forced to buy land parcels if sufficient wealthForced to sell land parcels if wealth < 0 Social neighbourhood FEARLUS Agents (von Neumann neighbourhood)

Decision algorithm Habit Satisfied with yield? Yes Yield Copying Majority Copying No Optimum Copying Copy neighbours? Yes Random No Parcel Matching

Year 12 Year 14 Year 16 Year 13 Year 15 Year 17 Experimentation and Imitation

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 > 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

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

Non-transitivity of winners II Why didn’t II beat SI? HRYI SI

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

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

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

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

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 don’t involve interactions with neighbours • Well suited to simple heuristic algorithms • Use only a small number of land parcels

Random Choice Manager Yield above Solvency Threshold Random Choice Manager Yield below Solvency Threshold 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

The Advantages of Diversity when all Land Uses are equal:Within-Estate Diversity in a Two-Parcel Environment Symmetrical Random Walk model • Both Parcels the same. • Two possible Land Uses, Yields of BET+1/2 and BET-1/2. • Equal probability each will be the “good” Land Use in any Year. • LPP 1.

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?

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?

£ 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

FEARLUS-W:Model 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 neighbour’s 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.

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 orconscious deliberation between alternatives”

Land Uses Pareto Front Multidimensional decision making Salience determines choice Estimated Profit Estimated Social Acceptability

Land Use LU 1 LU2 LU3 LU4 LU5 Mean Yield 7.5 8.5 9.5 10.5 11.5 Pollution 1 3 5 7 9 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:

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

Pollution No Social Approval With Social Approval No Reward No Reward No Social Approval With Social Approval Reward 50 @ 2000 Reward 50 @ 2000

Land Uses No Social Approval With Social Approval No Reward No Reward No Social Approval With Social Approval Reward 50 @ 2000 Reward 50 @ 2000

Decision Making Mode No Social Approval With Social Approval No Reward No Reward No Social Approval With Social Approval Reward 50 @ 2000 Reward 50 @ 2000

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

Other areas of decision-making in FEARLUS • Advice model • Allows agents to exchange cases when they don’t 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

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-interactivity is still an assumption • Mathematical tractability is no longer an excuse for failing to look formally at other assumptions and algorithms

Modelling evolving patterns of land use: The FEARLUS model