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Spatial Agent

by: Blanche Beier

Spatial Agent GEOG 631

Blanche Beier
GPA 3.96

Dawn Parker

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Dawn Parker
Class Notes
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This 6 page Class Notes was uploaded by Blanche Beier on Monday September 28, 2015. The Class Notes belongs to GEOG 631 at George Mason University taught by Dawn Parker in Fall. Since its upload, it has received 27 views. For similar materials see /class/215099/geog-631-george-mason-university in Geography at George Mason University.


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Date Created: 09/28/15
Intro Cont EVP631GEOG 631088645 Spatial Agentbased Models of Human environment Interactions Jan 30 2007 smmaw Emmngumva DawnPaikei mm MasanUnruersiv Plan for today Questions about de nitions etc Conceptual intro continued Patternoriented modeling Potential modeling platforms Market mechanism Logo models short labwriting assignment vamiAaM H E imamquot Leduvez s mmm GeaiveMasan UNI95W Course themes covered today Understanding the behavior of complex models 3rd way of science Model veri cation and validation esp patternoriented validation Empirical methods for building agent decision models someWnat more next week Modeling market interactions summer shaming tum pm mm mm Masan many Typology ofABMs BergerParker 2002 Jump to tables from new CASA report smmaw Emmngumva DawnPaikei mm Masanunwersiv Traditional scientific method smmam H E mm mmm GeaiveMas The third way of science SvaualABMH Eimaaaa Davm Varker eaaiaa Mas Pseudoinductive application to Cell 1 models Design hypothetical causal rulesmechanisms into ABM Generate database of outcomes by sweeping model parameter space i a a emergent macro outcomes as dependent variables and parameter values as independent Hypotheses equivalent of comparative statics or dynamics are contained in the estimate coef cients how to macro outcomes change as parameters change Dr Parker will show this with the SLUDGE model later Happe paper also takes a similar approach SvatialABM H E imam Ledurez DaanaMei GeaiveMasan mam Pseudoinductive application to Cell 4 models Parameterize ABM using realworld derived rules and data Generate one or a range ofmodel outcomes Analyze generated and realworld data using the same inductive model If estimated model coef cients are qualitatively similar coef cient sign and signi cance you have qualitative agreement with the real world If estimated model coef cients are statistically signi cantly similar you have quantitative agreement with the real world and I bet an incorrect model SvaualABMH Eimam Lama Davm Vaikei eaaiaa Masan mam But This is all open to discussion and we may change our mind SvatialABM H E imam Ledurez Davm Vamei eaaiaa Masan mam Issues in calibration verification and validation SvaualABMH Eimam Ledmez Dawmimi eaaiaa MasanUnnersm De nition Calibration I Calibration refers to the process of creating a model such that it is consistent With the data used to create the model Verbmg et a1 2006 Derivation of best t model parameters from real world data Davm Goodness of t measures of calibration only measure model applicability within the range of calibration data SvatialABM H E imam Ledurez DaanaMei GeaiveMasan mam De nitions Verification and Validation Veri cation and validation concem respectively the correctness of model construction and the truthfulness of a model with respect to its problem domain In other words veri cation means building the system right and valida ion means building the right systemO 0 model is veri e and works correctly then the modeler is concerned with validation comparing model outcomes to outside data and expectationsquot Manson from Parker et al 2003 spaualAaMH Eweneams LedmeZ Daanaikel Geaive Masanumversm Model Veri cation Most important for deductivelystnictured models mathematical programming and agentbased The main idea is that you must test out your model rules methodically to make sure they are doing what you intend them to Verification can also be similar to structural validationare the rules the right rules spatialABM u E lnteiadlans Ledulez omrmr GeaiveMasan Unwetsm Model validation Verburg et al 2006 Rykiel 1996 de nes validation as a demonstration within its domain of applicability possesses a satisfactory range of accuracy consistent With the intended application of the model Model validation is therefore the process of measuring the agreement between the mo el prediction and independent data If there is a good In ch then the method used to make the prediction is said to be valid It is crucial to distinguish between model calibration and model validation spaualAaMH Eweneams Ledulez om mu Geaive Masan Unwersnv Goals of validation Verburg et al 2006 Manson 2003 M nson answer question How well does a model characterize the target systemquot Verburg et al It is not particularly useful to attempt to wn amodel as valid or to condemn amodel as invalid should measure the erformance ofamodel in amanner that enables the scientist to know the level oftrust that one should put in the model Usefulvalidation should also give the modeller information necessary to improve the model spatialABM u E lnteiadlans Ledulez om mu Geaive Masan Unwetsm General validation critena Quote Manson 2003 from Kwansnicki 1999 Correctness model structure and outcomes must be similarto tnose ofthe target system Consistency tne model must be internally consistent and matcn tne conceptual trameworllt ll l orderto descnbe tnetarget system Universality tne model snould be applicable to circumstances beyond tnose descnbed by tne calibration data simplicity wnen cnooslng between two models all otnertnlngs being equal tne less complicated model is preferable Novelty a model snould create new knowledge or outcomes spaualAaMH Eweneams LedmeZ Daanaikel Geaive Masanumversm Competing measures Note there may be conflicts between these criteria Correctness may be more important for empirical case speci c models Novelty may be more important for generalizable theoretical models Manson notes p 61that the final structure of the model may be the outcome of interest spatialABM u E lnteiadlans Ledulez omrmr GeaiveMasan Unwetsm Structural vs outcome validation Structural validation examines whether the mechanisms in your model correctly represent realworld mechanisms are the exogenous components of your model representative of the stem under study Outcome validation asks whether model output endogenous components conform with data derived from the realworld system Outcome validation can be either spatial or a spatial today s articles mainly focus on spatial validation WWW Emmi tum mm itsin Mmin Patternoriented modeling Grimm et al 2005 Grimm forthcoming Key point is that validation targets are iden i ed a priori Targets are patterns emergent phenomena at multiple but higher scales Recall de nition of emergence from Auyang something about outcome patterns at higher scale that are qualitatively different that the lowerscale entities that produced them Rememberthe quilting example summon w E lnteiadlans twig omrmi GeaiveMasan unmw POM rationale Grimm et al 2005 Patterns represent underlying structure process and function Goal of POM is to identify underlying processes note similarity to concept of generative social science Epstein and Axtell Many processes could create the same single observed pattern Therefore multiple patterns at different scales are required Observed patterns should de ne research questions smian Elmeiaa ans teem om ma Geaive Masan umm Optimizing model complexity Need enough complexity not too much Approach I suggested last week focuses on identifying key sources of complexity in realworld systems Grimm et al also note that pattern outcomes of interest should guide model development your model hast be complex enough to produce the patterns you are interested in Both approaches should increase structural realism summon w E lnteiadlans twig om ma Geaive Masan unmw Patternmacroscale validation differing perspectives Grimm et al advocate model calibration based on higherscale patterns He proposes inverse modelingquot where behavioral rules are assumed and a calibration approach is taken to choosing the best parameters or set of parameters that can reproduce the patterns This approach could at least narrow dovm potential parameter sets or rule some out See Caruso et al 2005 for example smian Elmeiaa ans twig omrmi Geaive Masanunwersm Why do I worry about this approach tassurnes that the specified structure of the behavioral rnodel is correct OK fortrees but not so rn ch for humans There is a potential identi cation problem but multiple pattern outcomes may address tnis po nt you can tvalldate against outcome patterns that you have used for calibration Calibration can be highly path dependent ou would need a lot ofdata to use tnis approach to distinguish between competing behavioral models mm l do a knowledge tnis point and suggest strong inference to test alternative models but they don t relate tnis to the problem of calibration b t l n l w r d approach summon w E lnteiadlans twig omrmi GeaiveMasan unmw The home run goal Predicting something outside the range of model validation is a home run for example location of new archeological site Predicting a macroscale or emergent pattern not used for model development would be a world series win Examples smmaw Mumquot Ledmez mme mm Mme Return to tables for discussion of different simulation platforms spmmam H E mmquot Leduvez mum GeaiveMasan mew Modeling markets smmaw Mumquot mm pm mm mm Masan many Why mode markets Including markets can Increase the realism of ABMLUCC models Better re ect the incentives faced by agents Endogenoust regulate the number of agents d amount of occupied territory in the model Provide additional policyrelevant outputs to models Note Modeling markets does not require assuming perfectly rational profitmaximizing 5 mm H E mm tum behaV39or Dimmmww w may Potential EconomicMaiket Mechanisms for ABMLUCC Models Land markets Endogenous commodity prices Labor markets Fixed costs of adopting new land use Risk management and portfolio diversification Economies of Scale Capital markets lending and borrowing smmaw Mumquot Ledmez mme mm Mme Benefits of land market models More accurate modeling ofheterogeneous land values Development ofa more complex and realistic landscape Ability to address questions about market structuredistributions of land holdings Feedbacks between quantity location and pattern allows for adjustments based on neighborhood changes Modeling land abandonment See Polhill Parker Gotts ESSA 2005 AAG 2006 and forthcoming chmt rzfnmn tails pm my mum Endogenous Output Prices lfmarkets for a product are local the price received may ll as production increases This is very easy to model This endogenous price creates high payoffs for an innovatorifvery little is produce the price received for the very scarce commodity will be high Endogenous output prices also assure that some of that product will be produced in the landscape spaualAaMH Elmelaa ans Laura summer Gealve MasanUnnUSW Labor markets Most important in developing country models Berger Brazilian Amazon models A household s landuse decisions may be constrained by available labor ifthere is no labor market A labor market balances demands for additional labor for some households with supply of surplus labor by others resul ing in different production pattems Outside labor opportunities may also cilitate capital accumulation by households summon u E rruraurs Laura mm GeaiveMasan UNIvim Fixed Costs to Change Land Use Otten there are substantial startup costs when a manager changes land uses physical conversion new knowledge new equipment transition periods lf xed costs are present payof for conversion may need to be quite high and certain to induce conversion Fixed costs can explain managers who stick with less pro table strategies In general xed costs will slow the rate oftransitions in ABM mo e s spaualAaMH Elmelaa ans Laura Dalum Valkelr Gealve Masan Unwersm Risk Management and Portfolio Diversi cation rnout prlces outout prlces and Weather Thls nsk may lead managers to dlverslfythelroutputs By creatlrlg a portfollo of outputs that negatlvely coavary le when onoes for one are llkelyto be low onoes rorthe other are llkely to be hlgh farmers can reduce the overall rlsk for a glven pront threshold The availability of crop lnsuranoe may also play an lrnoortant role ln oeolslon makll lg lnoluslon ofrlsk can lead to fewertransltlorls and lnoreaseo heterogenelty orstrategles summon u E rruraurs Laura Dalum ma Gealve Masan UNIvim Economies of Scale If average cost ofproduction ll as farm size 39ncreases economies of scale are present Fixed costs lead to economies of scale Example tomato harvester in US also precision rming The large increase in rm size in the US has been widely attributed to economies of scale Including scale economies in models should also lead to more consolidation spaualAaMH Elmelaa ans Laura summer Gealve MasanUnnUSW Capital Markets Borrowing and Lending Access to capital plays an important role for rmers in terms ofinvestments to meet xed costs oftransitioning to new 39 risk outputs and managing Capital markets are also important for assessing the longmn value of an Investment Modeling capital markets can reveal linkages between interest rates and land management strategies If longterm investments play an important role in pro tability forwardlooking decision models are very important summon u E rruraurs Laura mm GeaiveMasan UNIvim


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