Simulator

The simulator consists of (a) submodels of the decision making of several groups, and (b) the impacted ecosystem as follows.

  1. the group of tiger poachers;
  2. the group of middleman involved in buying, transporting, and selling tiger parts;
  3. Chinese consumers of tiger parts;
  4. the Wildlife Crime Control Bureau within India's Ministry of Environment, Forests, and Climate Change (MoEFCC); and
  5. an individual-based submodel of Bengal tiger abundance in Bandhavgarh National Park, India.

The files contained in intelinfiles.zip are:.

  1. tigerpoleco.id: IntIDs file containing all submodel file names:
    (for tiger political-ecological system).
  2. tigergroups.dat, tigerregions.dat: Hold the system's associated formal and informal social groups, and associated region names.
  3. Submodel files of tiger poachers:
    ID input files: tigerpoachers.id, hypothesis parameter values: tigerpoachers-hyp.par, and
    initial parameter values: tigerpoachers-ini.par.
  4. tigermiddle.id, tigermiddle-hyp.par, tigermiddle-ini.par: Middleman group submodel files.
  5. Chinese consumers of tiger parts submodel files:
    tigerconsumers.id, tigerconsumers-hyp.par, tigerconsumers-ini.par.
  6. MoEFCC Crime Control Bureau submodel files:
    wccbureau.id, wccbureau-hyp.par, wccbureau-ini.par.
  7. Ecosystem submodel files. This submodel captures the population dynamics of those Bengal tigers living in India:
    tigereco.id, tigereco-hyp.par, tigereco-ini.par.
  8. Observed data files:
    tigers.dat (observed abundance), and tigerpesysobsacts.ahs (observed actions).

Example

To re-generate the actionable intelligence report, place the
report evaluate
   evaluate_social_network( start_time end_time Monte_Carlo_loops)
relation block just below the context files relation block in the file tigerpoleco.id. Then, run the report again with the following command. 

    idalone tigerpoleco.id

at a Windows command prompt. This report contains social network analysis measures that support the report's Detain, Surveil, and Interdict lists. The Detain list identifies the tiger trafficker who is doing the most damage to the ecosystem and hence is the most critical trafficker to detain.

Output

The actionable intelligence produced by this run produces the following output file, tigerintel.txt:

The criminal network just before arrests given in the Detain List are made:

The criminal network some weeks after these arrests:

How poaching's effect on the ecosystem is modeled

There is a causal chain in the tiger ecosystem's influence diagram that is pertinent to tiger poaching. This chain flows from the chosen management option through to tiger abundance. This chain is as follows.

Management Option (deterministic decision node with values beginning state, poach for cash, translocate animals) 

                     ↓

Poaching Activity (deterministic discrete node with values reduced activity, no change, increased activity) 

                     ↓

Poaching Pressure (stochastic discrete node with values minor, moderate, severe) 

                     ↓

Tiger Death Rate (stochastic continuously-valued node with values death rate ∈ (0, 1)) 

                     ↓

Tiger Abundance (stochastic continuously-valued node with values abundance ∈ (0, 3000)).

The last two nodes in this causal chain are part of a system of stochastic differential equations that model the population dynamics of tigers in Bandhavgarh National Park, India. The node representing the ecosystem's state that is readable by other simulator submodels is exclusively Tiger Abundance rather than the pair of nodes Number of Tigers Poached and Tiger Abundance. There is no node that is Number of Tigers Poached. Rather, the ecosystem submodel's Management Option has poach_for_cash as one of its possible values (see above). When this value is given as input to the ecosystem, poaching pressure is applied.

How SDE parameter values affect tiger abundance dynamics

The carrying capacity, birth rate, and death rate parameters are all specified in the files tigereco-hyp.par and tigereco-ini.par. These parameters will be adjusted when the simulator is fitted to a new data set of tiger abundance observations. Wide differences in tiger birth and death rates can cause tiger abundance to blow up as time progresses. If this happens or the solution exhibits very high variance, try forcing this rate difference to be zero as a first try at fixing the problem.

As exhibited above, poaching's effect on tiger abundance is expressed by its effect on the death rate parameter. But if initial carrying capacity is close to initial tiger abundance, abundance will be almost completely a function of prey abundance (herbivore abundance). In this situation, tiger birth and death rates will have little effect on tiger abundance. To make abundance be sensitive to these rates, set the initial carrying capacity to be at least four times the initial abundance of tigers.

Rising Stars of the WTS and its Network Resiliency Index

This Tool assumes that the confederation gathers evidence on the WTS at three different times. The first time is to find out the size, connectivity, and assets of the current, undisturbed WTS. The confederation quietly watches the network for several weeks and at the end of that period, observes its size and connectivity again. Then, the confederation recommends to law enforcement those WTS players to detain, surveil, and interdict. Finally, some weeks after these arrests, the confederation gathers information on the size and connectivity of the recovering WTS. Call these three time points, t1, t2, and t3.

The file tigerintel.txt contains predictions of those players in the WTS who are predicted to move into leadership roles (called Rising Stars); and the resiliency of the WTS (a measure of how fast the syndicate's functionality can recover from a series of player removals). All of these terms are discussed in Haas and Ferreira (2015). The mathematical forms of the Rising Stars and resiliency algorithms are at Environmental Management submission

Data sets used to statistically fit the simulator's parameters

Political-Ecological actions history data set

The file polecotigers.dat contains observations on political-ecological actions. This file is as follows. 


comment Political-Ecological Actions Data
comment Date Actor          Action
begin
03-15-11  tigerpoacher1  poach_for_cash
11-05-12  tigerpoacher2  poach_for_cash
07-30-11  wccbureau      arrest_poachers
end

The values of the simulator's parameters have been statistically estimated using these two combined data sets. To re-run the statistical estimation computation, place the
report estimate relation block just below the context files relation block in the file tigerpoleco.id. Then, run the computation again with the following command. 

    idalone tigerpoleco.id

Data on ecological nodes

The file obstigers.dat contains observations on ecological nodes in the ecosystem influence diagram. This file is as follows. 


comment Bengal tiger abundance estimates
Region Time TigerAb
begin
Bandhavgarh 2010 1466
Bandhavgarh 2011 1621
Bandhavgarh 2012 1495
Bandhavgarh 2013 2968
Bandhavgarh 2014 2619
Bandhavgarh 2015 2875
Bandhavgarh 2016 3235
Bandhavgarh 2017 3235
Bandhavgarh 2018 3235
Bandhavgarh 2019 3235
Bandhavgarh 2020 3235
end

This data would be collected either by hired field ecologists or through technological methods such as spoor sightings and/or camera traps.