Tag Archives: PDG Control

Active adaptive management solutions: Belief SDP

Have finally got­ten my active adap­tive man­age­ment solu­tions work­ing. The active adap­tive man­age­ment solu­tion learns very quickly and rather monot­o­n­i­cally which model is cor­rect, even when mod­els dif­fer by small amounts and the ini­tial belief is heav­ily skewed to the wrong model. I’ve imple­mented exam­ples with Myers model, but in this para­me­ter space the allee

Policy costs to changing mangement, TP 1b: Apples to Apples

A cen­tral ques­tion of train­ing prob­lem 1b addressed at the work­ing group was how to com­pare the dif­fer­ent penal­ties. Fol­low­ing up on those dis­cus­sions, here are a few notes on how far I’ve got­ten in the analy­sis regard­ing the best way to make these com­par­isons. simulate_npv_curves Nice job of show­ing the per­for­mance of the vari­ance

Tuesday: active adaptive management, a first solution

Spent way more of today than I wanted to ham­mer­ing out the active adap­tive man­age­ment imple­men­ta­tion for a triv­ial model-choice prob­lem using the dis­cretized belief-SDP approach. We have alter­nate mod­els \(f_1\) and \(f_2\) of the state equa­tions (pop­u­la­tion growth dynam­ics) \[ x_{t+1} = z_t f(x_t) \] and intro­duce a con­tin­u­ously val­ued belief prob­a­bil­ity \(p\) that

PDG Control Day 1 goals by training problem

Train­ing Prob­lem I / Theme 1 Pol­icy costs Func­tional forms of cost & Struc­ture of the paper Com­pa­ra­bil­ity (view over all costs? view expected eco­nomic ben­e­fit?) Visu­al­iza­tions Dan’s L1 norm story (vs sto­chas­tic, dis­crete time?) Trans­ac­tion Fee details in cts time, deter­min­is­tic Advanced prob­lem 1 How many spa­tially dif­fer­en­ti­ated poli­cies are opti­mal 1. for­mu­late bioe­co­nomic model 2. mod­u­lar­ity meth­ods 3.

PDG Control, Training Problem 2 update to group

Intrin­sic Sto­chas­tic­ity \[ x_{t+1} = z_t f(x_t, h_t) \] \[ max_{h_t} \textrm{E} \left( \sum_t \Pi(h_t, x_t) \delta^t \right) \] Mea­sure­ment error \( m_t = z_m x_t \) Imple­men­ta­tion error \( i_t = z_i h_t \) Next: Uncer­tainty = we can learn and hence decrease the uncer­tainty. But state-space grows expo­nen­tially. Para­met­ric Uncer­tainty Bio­log­i­cal state equa­tion \[

Parameter uncertainty in stochastic control problems

Day 3 of my short-term vis­i­tor stay before the work­ing group starts. Wednes­day: Travel Thurs­day: 8:45 Group Meet­ing: Paul, Michael, Lance, Dan, Jake, Carl. Train­ing prob­lem II dis­cus­sion — prob­lem tax­on­omy: sto­chas­tic , model uncer­tainty, para­me­ter uncer­tainty, state uncer­tainty. Learn­ing on uncer­tainty (passive/adaptive active man­age­ment) in the uncer­tainty cases, all of which increase the para­me­ter

Monday

Triv­ial exam­ple of a non-optimal but more robust pol­icy by edit­ing the opti­mal strat­egy directly. Note in par­tic­u­lar that this out­per­forms the opti­mal strat­egy eco­nom­i­cally as well as eco­log­i­cally (fewer crashed pop­u­la­tions). Run­ning the policy-costs model with Bev­er­ton Holt instead of the Myers func­tion seems to give a clearer pic­ture of a sys­tem respond­ing to

Thursday, Friday, Saturday

for­age fish sem­i­nar — Patrick and Jay with inter­est­ing stuff. Sounds like com­pen­sat­ing ben­e­fits due to quality-response (larger fish) and stock effect (eas­ier to find) will be more minor in this fish­ery, but demand response be com­pen­sat­ing. knit hooks exter­nal­iza­tion exam­ple work­ing (based on Reed.R). Still unsure how I feel about exter­nal­iza­tion vs fully lit­er­ate

Stochastic optimization

Sur­pris­ing het­ero­gene­ity of profit dis­tri­b­u­tion under a sim­ple Beverton-Holt model Usu­ally I expect the alter­nate sta­ble state dynam­ics to be the ones pro­duc­ing bimodal dis­tri­b­u­tion of profit: The pat­tern is dri­ven by a costs to har­vest­ing mak­ing har­vest unprof­itable most of the time, while chance fluc­tu­a­tions push some pop­u­la­tions across this thresh­old again and again.

Monday

PDG Con­trol, aside exer­cise Lab group com­mented that opti­mum in Reed model looks unre­al­is­tic due to fre­quency with which it shuts down all fish­ing. This is an arti­fact of not hav­ing extrac­tion costs increase suf­fi­ciently for small catch, and can be eas­ily fixed by increas­ing that cost (c in the profit eq, profits-costs = p*x-c/x)