Week+2

Craig Kasemodel Week 3 - Chaps 3-5
 * CS699 - Agent Based Modeling**


 * Chap 3, p45**

1. In **//Using The ODD Protocol for Describing Three agent-based Social Simulation Models of Land-use Change//**, the authors describe modeling of Land-use change using the ODD model. The authors look at individual based models and agent-based models. Individual based models differ from agent-based models by modeling nonhuman interactions with an ecological system whereas agent based models model human actors and decisions. The authors description of the overview, design, and details protocols is quite in-depth. This paper should serve as a model for using ODD protocols and IBM/ABMs (Polhill, Parker, Brown, & Grimm, 2008).

2. In **//State-dependent foraging rules for social animals in selfish herds//**, the authors attempt to describe selfish herding behavior by modeling the trade-offs of reducing predation risk by herding while maximizing foraging energy efficiency. While they did use NetLogo v.1.3 to model individuals behavior, they did not describe the ODD protocols or their model in-depth. The authors did include a nice time-step diagram of the individuals decision making. If I were to model caribou foraging behavior in Alaska, I would follow their state-dependent optimality theory, even if the details of the model were not lucid (Rands, Pettifor, Rowcliffe, & Cowlishaw, 2004).

3. State Variables = total number of caribou in the herd; number of caribou bands; number of wolves; number of wolf packs; number of alternative prey; environmental factors = ave snow depth, ave snow duration, temperature, Spatial units = soil type, elevation, veg cover, lichen, Collectives = caribou bands, wolf packs || The model = 1000 ha^2 || (Grimm et al., 2010)
 * || ** Elements of the ODD Protocol ** ||
 * ** Overview ** || 1. Purpose - Modeling the Migration patterns and Populations of Alaskan caribou herds (Mulchatna herd). Are their movements currently affected by predation or forage availability? Will future movements be affected by increased human activity (Pebble Mine)? Are caribou numbers affected by active predator management? ||
 * ^  || 2. Entities, state variables, and scales - Entities = caribou; wolves; lichen/forage; alternative prey
 * ^  || 3. Process overview and scheduling = 1 time step = 1 month; 1 simulation = 100 years; 1 grid = 1 ha;
 * ** Design Concepts ** || 4. Design concepts ||
 * ^  || * Basic principles - optimal foraging theory within social herding for caribou; predator - prey relationships defined by Lotka–Volterra equations; multiple equilibrium theory (predator release); human disturbance theory; mine pollution effects
 * Emergence - prey switching?
 * Adaptation - caribou move to the cell with the greatest quantity of forage
 * Objectives - twining rates of caribou
 * Learning - wolves learn to avoid areas of human activity; caribou might move toward areas of human activity if the environment isn't negatively impacted; wolves learn about yearly caribou migration patterns
 * Prediction
 * Sensing - wolf pack territory & size
 * Interaction - wolf dispersal, wolf predation on caribou, wolf predation on alternate prey, predator management
 * Stochasticity - environmental factors = snow depth & duration, winter temperatures (crusty snow?)
 * Collectives - wolf packs, caribou bands within the herd
 * Observation - caribou population and bou pop growth rates ||
 * ** Details ** || 5. Initialization = @ t=0; historical rates for bou and wolves (adfg) ||
 * ^  || 6. Input data = yes; model uses annual snow measurements and yearly duration totals (freeze to break-up) ||
 * ^  || 7. Submodels = time steps are monthly to take into account migration patterns from winter range to calving to summer to winter, typically in some circular motion ||
 * ** Details ** || 5. Initialization = @ t=0; historical rates for bou and wolves (adfg) ||
 * ^  || 6. Input data = yes; model uses annual snow measurements and yearly duration totals (freeze to break-up) ||
 * ^  || 7. Submodels = time steps are monthly to take into account migration patterns from winter range to calving to summer to winter, typically in some circular motion ||
 * ** Details ** || 5. Initialization = @ t=0; historical rates for bou and wolves (adfg) ||
 * ^  || 6. Input data = yes; model uses annual snow measurements and yearly duration totals (freeze to break-up) ||
 * ^  || 7. Submodels = time steps are monthly to take into account migration patterns from winter range to calving to summer to winter, typically in some circular motion ||
 * ** Details ** || 5. Initialization = @ t=0; historical rates for bou and wolves (adfg) ||
 * ^  || 6. Input data = yes; model uses annual snow measurements and yearly duration totals (freeze to break-up) ||
 * ^  || 7. Submodels = time steps are monthly to take into account migration patterns from winter range to calving to summer to winter, typically in some circular motion ||
 * ^  || 7. Submodels = time steps are monthly to take into account migration patterns from winter range to calving to summer to winter, typically in some circular motion ||

Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S. F. (2010). The ODD protocol: A review and first update. //Ecological Modelling//, //221//(23), 2760–2768. doi:10.1016/j.ecolmodel.2010.08.019 Polhill, J. G., Parker, D., Brown, D., & Grimm, V. (2008). Using the ODD Protocol for Describing Three Agent-Based Social Simulation Models of Land-Use Change. //Journal of Artificial Societies and Social Simulation//, //11//((2) 3), 3. Retrieved from http://jasss.soc.surrey.ac.uk/11/2/3.html Rands, S. A., Pettifor, R. A., Rowcliffe, J. M., & Cowlishaw, G. (2004). State–dependent foraging rules for social animals in selfish herds. //Proceedings of the Royal Society of London. Series B: Biological Sciences//, //271//(1557), 2613 –2620. doi:10.1098/rspb.2004.2906


 * Chap 4, p59**

1. n = 50; q =.50 : n = 50; q =0.0 : movements will be completely random - yes, movt were in all quadrants n = 50; q = 1.0 ; all 50 will go to the big hill (elev1) - yes, but was surprised all 50 moved in a same straight line

2. crt 50 [ set size 2 pen-down ]
 * creating 50 butterflies for now
 * setxy random-pxcor random-pycor **
 * set intiial location of butterflies

reset-ticks

set q 0.2
 * initialize the "q" parameter

end
 * end of setup procedure

3. As long as q<0.0; butterfly movement will advance in a relatively straight line to the hill tops and since the hill are set up with smooth elevation gradients, there isn't any variation in their path movements once they begin the uphill climb (function of nearest neighbor patch analysis)

4. to setup ca ; clear all variables to zero at start of new iteration

ask patches ; Assign an elevaion to patches and color them by it
 * Elevation decrease linearly with distance from the center of hills. Hills are at (30, 30) and (120, 100).
 * The first hill is 100 units high. The second hill is 50 units high.

[ let elev1 100 - distancexy 30 30 let elev2 50 - distancexy 120 100

ifelse elev1 > elev2 [set elevation elev1] [set elevation elev2]


 * set elevation elevation + random 40 **

set pcolor scale-color green elevation 0 100 ]
 * end of"ask patches"

>> Adds realism to the model by adding landscape variation (varying patch elevation gradients)

Chap 5, p72-73