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Travellers dilemma (Ariel Rubenstein 2004) Imagine you are one of the players in the following two-player game: Each of the players chooses an amount between.

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Presentation on theme: "Travellers dilemma (Ariel Rubenstein 2004) Imagine you are one of the players in the following two-player game: Each of the players chooses an amount between."— Presentation transcript:

1 Travellers dilemma (Ariel Rubenstein 2004) Imagine you are one of the players in the following two-player game: Each of the players chooses an amount between $180 and $300 Both players are paid the lower of the two chosen amounts Five dollars are transferred from the player who chose the larger amount to the player who chose the smaller one. In the case that both players choose the same amount, they both receive that amount and no transfer is made. How much would you choose?

2 Ex ante testing of carbon trading policies in the SA Murray Darling Basin John Ward, Brett Bryan, Darran King, Neville Crossman June 2007

3 Context  The SA region of the Murray Darling Basin has seen over 80 years of land clearance and agricultural production  Prominent signs of environmental degradation – Surface and ground Water, Water quality, Land, Biota  Government policy - Integrated Natural Resource Management  INRM plan based on resource condition targets and actions  SA MDB Integrated NRM targets are multi-objective and include: Biodiversity River Salinity Wind Erosion  Establish on-ground investment priorities for NRM actions on private land  Evaluate the potential of market based Instruments

4 Research programme: calibrating multi-agent models for policy optimisation in the SA MDB 1.Identify and evaluate market based incentives to encourage revegetation 2.At the farm scale, estimate the economic viability and contribution to resource targets of biomass energy and carbon trading 3.Survey dryland farmers to elicit farming styles and describe relationships between current community attitudes and land management actions. 4.Use experimental economics to quantify behavioural responses by landholders to market incentives in revegetation decision environments 5.Use the survey and experimental data to calibrate a multi-agent dynamic simulation of revegetation actions over fifty years 6.Implement four simulation scenarios of revegetation policy which estimate carbon, natural resource and economic outcomes 7.Describe the relationships between policy variables and NRM and economic outcomes to inform policy making processes prior to implementation.

5 Economic Viability Carbon Mallee community - €10 / tonne Mallee community - €20 / tonne Mallee community - €30 / tonne Mallee community - €40 / tonne Viable areas - €10 / tonne Viable areas - €20 / tonne Viable areas - €30 / tonne Viable areas - €40 / tonne One € = A$1.62

6 Economic Viability of Carbon

7 The quest for a behavioural epsilon: which version of “rational behaviour” to model? H.economicus H.reciprocans H.psychologicus Chi squared test of experimental cluster frequencies cf with survey sample= 0.659 (not sig dif α =0.05)

8 Homo reciprocans, Homo psychologicus “No society would be viable without norms and rules of conduct. Such norms are necessary for viability exactly in fields where strict economic incentives are absent or cannot be created” Sen (1977). H.economicus Egoist H. reciprocans Altruist Not dichotomous but a continuum (plurality of values or multiple motivations) Self regardingOther regarding Welfarist canonNon-Welfarist canon Familial ties and relationships Group claims Peer groups: prestige and reputation Social class Local community: avoiding sanctions Ethical beliefs Non-kin responsibilities H. psychologicus Information losses arise which are random and non-trivial Aggregating individual behaviour can’t reliably be made when non-systematic or non- correlated variance is observed in preference behaviour Result is deficient or babbling equilbria of uncompensated interdependencies

9 Survey of farmer attitudes and behaviours Problem: what is the willingness and capacity of landholders to participate or adopt?  Influenced by the level of social resistance, risk vector, relative advantage, ease of implementation, innovation and motivations for farmers to change land managing actions to revegetation based enterprises Based on Ajzens theory of planned behaviour (1991): describes actual revegetation behaviour from existing attitudes and behavioural intent Aim of questionnaire  Elicit existing attitudes and behaviours as farming styles  Predict behaviour as a function of an attitude vector and revegetation intent  Impute functions as 1 st iteration of a policy optimising agent based model Modified Dillman method to administer mail back questionnaire:  Sent to all 1,084 dryland farmers >10 ha in the SA MDB  593 responses: 56% response rate,  44 agreed to further participation

10 Attitudinal Scales  Business  Environmental attitude  Environmental responsibility  Technology Innovation  farming priority  Individual knowledge  Planning risk  Tradition  Learning preferences  Perceived control capital  Perceived control empowerment  Perceived control social norms  Perceived control time Behaviours  Planning, accounting, computers  Farm & soil management, selling, markets  Sowing and grazing practices  Vegetation management  Planting and remnant aspirations  Scheme participation (e.g. Landcare) Attitudes  Salinity on farm and MDB,  Type of land use- conservation and economic outcomes for SA MDB Spatial relationships  behaviour of k nearest neighbour  spatial correlation (regional and local)  spatial clustering, lag and error Survey instrument Demographics  age, time on farm, time farm in family  income data, farm equity, financial position  self description, education, area of farm

11 Frequency% 129751.9 214425.2 36810.1 48412.8 Total593100.0 Principle components factor analysis and hierarchical cluster analysis socially influenced farmers innovative farm business managers life style hobby farmers time and capital constrained conservation managers

12 Cluster spatial (centroid) distribution RBi = Att i + I  i + Sn  i + PC i + Opp i + wB j Where for land holder i: RB: represents current revegetation behaviour Att i : represents vector of attitudes I  i : represents intended reveg action Sn  i : represents influence of social norms on i decision making PC i : represents a vector of perceived controls Opp i : represents current opportunity cost w  j : represents decayed weighted influence of nearest neighbour j for behaviour  and w = 1/distance i-j

13 Experimental economics: Hypothesis testing Hypothesis 1: H A  providing a map of neighbours’ management decision (as a visual cue) will change experimental decision choices (particularly cluster group 1 or socially influenced decision makers) Hypothesis 2: H A  decisions to revegetate and therefore the volume of traded carbon and farm income will increase as a result of providing a visual cue of neighbours’ actions.

14 Experimental design and metrics Carbon market treatments Numerical decision information only No visual cue Decision information Visual cue (map of all farm decisions) Waikerie 1 session (10 periods) 1 session Murray Bridge 1 session Typical experimental farm Experimental decisionIncome /10 ha carbon t/10ha optimal $/10 ha @ $50 /t carbon marginal change in income marginal change in carbon marginal value carbon $/t traditional111560 00 biofuels220630 -9070 biomass377171130386754 trad + native veg45781513345781538 native veg5030151111563038 Experimental metrics Individual and aggregate carbon production Individual and aggregate income: Player payments Decision making Market behaviour

15 Carbon trading experimental sessions

16 Farm decision making: visual cue (map) of all catchment decisions

17 Experimental results

18 Dynamic simulations of SA MDB revegetation Four farm scale decision making scenarios  Where an individual agent selects one cleared ha per annum to revegetate for a period of 50 years Random Lowest opportunity cost to highest Highest biodiversity value to lowest According to social diffusion.  If neighbour revegetates, then agent revegetates (influence is a decaying distance function)  assumes 5% are innovators and the probability of revegetation for all agents increases with time.

19 Dynamic decision outcomes: 50 years

20 Bringing empirically based behavioural data into NRM policy testing 1.Calibrate the social diffusion model based on empirical data  Higher initial levels of innovation (31% not 5% as previously assumed)  Quantify policy that includes dissemination of catchment wide decisions  Quantify variable learning capacities, responses and transaction costs of novel choices  Complement pre-existing norms and institutions 2.Effect on policy performance by targeting observed farming segments  Enumerate the effects of policy that matches the motivations of cluster segments: attitudinal and temporal sequencing 3.Ex ante modelling of policies that address the likely effects of global warming: addressing regional vulnerability and resilience

21 Waikerie carbon trading

22 Murray Bridge carbon trading

23 Total recharge results a a ac d cd e e Dunnett’s T3 post hoc test: Homogeneity of variance (Levine statistic) p < 0.05; ANOVA coefficients: F (7, 142) = 98.600; p< 0.05; Treatment means with the same letter were not statistically different at  =0.05

24 “Life is animated water” (Vernadsky 1986) Email: Phone 83038685

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