Luis R. Izquierdo, Segismundo S. Izquierdo, José Manuel Galán and José Ignacio Santos (2009)
Techniques to Understand Computer Simulations: Markov Chain Analysis
Journal of Artificial Societies and Social Simulation
vol. 12, no. 1 6
<http://jasss.soc.surrey.ac.uk/12/1/6.html>
For information about citing this article, click here
Received: 16Apr2008 Accepted: 10Sep2008 Published: 31Jan2009
Applet 1

Applet 1. Applet designed to illustrate the fact that a pseudorandom number generator always produces the same sequence of pseudorandom numbers when given the same random seed. The button "Clear" initialises the model, setting the random seed to 0. The user can select a specific random seed by clicking on "Change random seed", or ask the computer to generate one automatically (based on the current date and time) by clicking on "Computergenerated seed". Clicking on the button labelled "Generate list of pseudorandom numbers" shows a list of three pseudorandom numbers drawn from a uniform distribution between 0 and 1. This applet has been created with NetLogo 4.0 (Wilensky 1999) and its source code can be downloaded here. 
Figure 1. Snapshot of CoolWorld. Patches are coloured according to their temperature: the higher the temperature, the darker the shade of red. The white labels on each patch indicate the integral part of their temperature value. Houses are coloured in orange and walkers (represented as a person) are coloured in green. 
The value of probleavinghome and probrandommove is shared by every walker in the model. There is no restriction about the number of walkers that can stay on the same patch at any time.
Figure 2. Snapshot of CoolWorld. Patches are coloured according to their temperature: the higher the temperature, the darker the shade of red. Houses are coloured in orange, and form a circle around the central patch. Walkers are coloured in green, and represented as a person if standing on a patch without a house, and as a smiling face if standing on a patch with a house. In the latter case, the white label indicates the number of walkers in the same house. 
Figure 3. Relative frequency distribution of the number of walkers in a house after 50 timesteps, obtained by running CoolWorld 200 times, with the initial conditions described in the text. 
Figure 4. In blue: Relative frequency distribution of the number of walkers in a house after 50 timesteps, obtained by running CoolWorld 100 times (Battery A), with the initial conditions described in the text. In grey: Exact probability function (calculated using Markov chain analysis). 
Figure 5. In blue: Relative frequency distribution of the number of walkers in a house after 50 timesteps, obtained by running CoolWorld 100 times (Battery B), with the initial conditions described in the text. In grey: Exact probability function (calculated using Markov chain analysis). 
Figure 6. In blue: Relative frequency distribution of the number of walkers in a house after 50 timesteps, obtained by running CoolWorld 50 000 times (Battery A), with the initial conditions described in the text. In grey: Exact probability function (calculated using Markov chain analysis). 
Figure 7. In blue: Relative frequency distribution of the number of walkers in a house after 50 timesteps, obtained by running CoolWorld 50 000 times (Battery B), with the initial conditions described in the text. In grey: Exact probability function (calculated using Markov chain analysis). 
Figure 8. Schematic transition diagram of a Markov chain. Circles denote states and directed arrows indicate possible transitions between states. In this figure, circles and arrows coloured in red represent one possible path where the initial state X_{0} is s_{8} and the final state is s_{2}. 
Implicitly, our definition of transition probabilities assumes two important properties about the system:
P(X_{n}_{+1} = x_{n}_{+1}  X_{n} = x_{n}, X_{n}_{—1} = x_{n}_{—1},…, X_{0} = x_{0}) = P(X_{n}_{+1} = x_{n}_{+1}  X_{n} = x_{n})
P(X_{n}_{+1} = j  X_{n} = i) = P(X_{n} = j  X_{n}_{—1} = i) = p_{i}_{,j}
Applet 2

Applet 2. Applet of a 1dimensional random walk. Patches are placed in a horizontal line at the topright corner of the applet; each of them is labelled with a red integer. Pressing the button labelled "Create Walker" allows the user to create one single random walker, by clicking with the mouse on one of the patches. Clicking on "go once" will make the random walker move once, while "go" asks the random walker to move indefinitely. The plot beneath the patches shows the time series of the random walker's position. Patches are coloured in shades of blue according to the number of times that the random walker has visited them: the higher the number of visits, the darker the shade of blue. This applet has been created with NetLogo 4.0 (Wilensky 1999) and its source code can be downloaded here. 
Figure 9. Transition diagram of the model shown in Applet 2. Each yellow circle represents a state of the system, with the number inside denoting the patch number. Arrows between states show possible transitions between states. Every arrow has a blue label that indicates the probability with which that transition takes place. 
(1) 
Where, as explained above, p_{i}_{,j} is the probability P(X_{n}_{+1} = j  X_{n} = i) that the system will be in state j in the following timestep, knowing that it is currently in state i.
It can be shown that one can easily calculate the transient distribution in timestep n, simply by multiplying the initial conditions by the nth power of the transition matrix P.
Proposition 1. a^{(n)} = a^{(0)} · P^{n}.
Thus, the elements p^{(n)}_{i,j} of P^{n} represent the probability that the system is in state j after n timesteps having started in state i, i.e. p^{(n)}_{i,j} = P(X_{n} = j  X_{0} = i). A straightforward corollary of Proposition 1 is that a^{(n+m)} = a^{(n)} · P^{m}.
As an example, let us consider the 1dimensional random walk implemented in Applet 2. Imagine that the random walker starts at a random initial location, i.e. a^{(0)} = [1/17, …, 1/17]. The exact distribution of the walker's position in timestep 100 would then be a^{(100)} = a^{(0)} · P^{100}. This distribution is represented in Figure 10, together with an empirical distribution obtained by running the model 50 000 times.
Figure 10. Probability function of the position of Applet 2's random walker in timestep 100, starting at a random initial location. 

Figure 11. Transient distributions of the location of a single walker in a CoolWorld model where the temperature profile and the distribution of houses are as described in paragraph 5.8. The height of each patch denotes the relative probability that the walker is on the patch in any given timestep. In other words, the plot uses height to represent a^{(n)} for each timestep n. 
Note that j is accessible from i ≠ j if and only if there is a directed path from i to j in the transition diagram. In that case, we write i→j. If i→j we also say that i leads to j. As an example, in the THMC represented in Figure 8, s_{2} is accessible from s_{12} but not from s_{5}. Note that the definition of accessibility does not depend on the actual magnitude of p^{(n)}_{i,j} , only on whether it is exactly zero or strictly positive.
If i communicates with j we also say that i and j communicate and write i↔j. As an example, note that in the simple random walk presented in paragraph 6.4 (see Applet 2) every state communicates with every other state. It is worth noting that the relation "communication" is transitive, i.e.
{i↔j , j↔k} ⇒ i↔k.
{i ∈ C, j ∈ C } ⇒ i↔j
{i ∈ C, i↔j} ⇒ j ∈ C
As an example, note that in the simple random walk presented in paragraph 6.4 there is one single communicating class that contains all the states. Similarly, any CoolWorld model where probleavinghome ∈ (0, 1) and probrandommove ∈ (0, 1) has one single communicating class containing all the possible states. In the THMC represented in Figure 8 there are 4 communicating classes: {s_{2}}, {s_{5}}, {s_{10}}, {s_{1}, s_{3}, s_{4}, s_{6}, s_{7}, s_{8}, s_{9}, s_{11}, s_{12}}.
Note that once a Markov chain visits a closed communicating class, it cannot leave it. Hence we will sometimes refer to closed communicating classes as "absorbing classes". This latter term is not standard in the literature, but we find it useful here for explanatory purposes. Note that if a Markov chain has one single communicating class, it must be closed.
As an example, note that the communicating classes {s_{10}} and {s_{1}, s_{3}, s_{4}, s_{6}, s_{7}, s_{8}, s_{9}, s_{11}, s_{12}} in the THMC represented in Figure 8 are not closed, as they can be abandoned. On the other hand, the communicating classes {s_{2}} and {s_{5}} are indeed closed, since they cannot be abandoned. When a closed communicating class consists of one single state, this state is called absorbing. Thus, s_{2} and s_{5} are absorbing states. Formally, state i is absorbing if and only if p_{i,i} = 1 and p_{i,j} = 0 for i ≠ j.
S = C_{1} ∪ C_{2} ∪ … ∪ C_{k} ∪ T where C_{1}, C_{2}, …, C_{k} are closed communicating classes, and T is the union of all other communicating classes.
Note that we do not distinguish between nonclosed communicating classes: we lump them all together into T. Thus, the unique partition of the THMC represented in Figure 8 is S = {s_{2}} ∪ {s_{5}} ∪ {s_{1}, s_{3}, s_{4}, s_{6}, s_{7}, s_{8}, s_{9}, s_{10}, s_{11}, s_{12}}. Any CoolWorld model where probleavinghome ∈ (0, 1) and probrandommove ∈ (0, 1) has one single (closed) communicating class C_{1} containing all the possible states, i.e. S ≡ C_{1}. Similarly, all the states in the simple random walk presented in paragraph 6.4 belong to the same (closed) communicating class.
As an example, note that every state in the simple random walk presented in paragraph 6.4 is periodic with period 2. On the other hand, every state in any CoolWorld model where probleavinghome ∈ (0, 1) and probrandommove ∈ (0, 1) is aperiodic.
An interesting and useful fact is that if i↔j , then i and j must have the same period (see theorem 5.2. in Kulkarni (1995)). In particular, note that if p_{i,i} > 0 for any i, then the communicating class to which i belongs must be aperiodic. Thus, it makes sense to qualify communicating classes as periodic with period d, or aperiodic. A closed communicating class with period d can return to its starting state only at times d, 2d, 3d, …
Proposition 3 states that sooner or later the THMC will enter one of the absorbing classes and stay in it forever. Formally, for all i ∈ S and all j ∈ T: , i.e. the probability of finding the process in a state belonging to a nonclosed communicating class goes to zero as n goes to infinity. Naturally, if the initial state already belongs to an absorbing class C_{v}, then the chain will never abandon such a class. Formally, for all i ∈ C_{v} and all j ∉ C_{v}: p^{(n)}_{i,j} = 0 for all n ≥ 0. We provide now two examples to illustrate the usefulness of Proposition 3.
Figure 12. Average number of walkers in each patch over timesteps 0 to 10 000 calculated with one single run (i.e. an estimation of the mean of the occupancy distribution for each patch, assuming 10 000 timesteps are enough) in a CoolWorld model parameterised as described in paragraph 5.8. 
Figure 13. Average number of walkers in each patch in timestep 1000, calculated over 1000 simulation runs (i.e. an estimation of the mean of the limiting distribution for each patch, assuming 1000 timesteps are enough) in a CoolWorld model parameterised as described in paragraph 5.8. 
Applet 3

Applet3. A model of reinforcement learning for 2 × 2 games. The white square on the top right is a representation of the state space, with player 1's propensity to cooperate in the vertical axis and player 2's propensity to cooperate in the horizontal axis. Thus, each patch in this square corresponds to one state. The red circle represents the current state of the system and its label (CC, CD, DC, or DD) denotes the last outcome that occurred. Patches are coloured in shades of blue according to the number of times that the system has visited the state they represent: the higher the number of visits, the darker the shade of blue. The plot beneath the representation of the state space shows the time series of both players' propensity to cooperate. This applet has been created with NetLogo 4.0 (Wilensky 1999) and its source code can be downloaded here. 
where p^{n}_{r,action} is player r's propensity to undertake action action_{r} in timestep n, and u_{r}(o^{n}) is the payoff obtained by player r in timestep n, having experienced outcome o^{n}. The updated propensity for the action not selected derives from the constraint that propensities must add up to one. Note that this model can be represented as a THMC by defining the state of the system as a vector containing both players' propensity to cooperate, i.e. [p_{1,C} , p_{2,C}]. The following subsections analyse models where T_{r} > R_{r} > P_{r} > A_{r} > S_{r}, r = 1, 2.

Figure 14. Transient distributions of the reinforcement learning model without trembling hands noise. Each patch represents a certain state of the system [p_{1,C} , p_{2,C}]. The closest patch to the vertical axis on the left represents the state where both players' propensity to cooperate is 0. The axis departing away from us from the origin denotes player 1's propensity to cooperate. The other axis (coming towards us) denotes player 2's propensity to cooperate. The height of each patch denotes the probability that the system is on the state represented by the patch in any given timestep. In other words, the plot uses height to represent the distribution of X_{n} for each timestep n. 

Figure 15. Transient distributions of the reinforcement learning model with trembling hands noise equal to 0.1. Each patch represents a certain state of the system [p_{1,C} , p_{2,C}]. The closest patch to the vertical axis on the left represents the state where both players' propensity to cooperate is 0. The axis departing away from us from the origin denotes player 1's propensity to cooperate. The other axis (coming towards us) denotes player 2's propensity to cooperate. The height of each patch denotes the probability that the system is on the state represented by the patch in any given timestep. In other words, the plot uses height to represent the distribution of X_{n} for each timestep n. 
^{2}Note that simulations of stochastic models are actually using pseudorandom number generators, which are deterministic algorithms that require a seed as an input.
^{3}The mere fact that the model has been implemented and can be run in a computer is a proof that the model is formal (Suber 2007).
^{4}As a matter of fact, strictly speaking, inputs and outputs in a computer model are never numbers. We may interpret strings of bits as numbers, but we could equally well interpret the same strings of bits as e.g. letters. More importantly, a bit itself is already an abstraction, an interpretation we make of an electrical pulse that can be above or below a critical voltage threshold.
^{5}A sufficient condition for a programming language to be "sophisticated enough" is to allow for the implementation of the following three control structures:
^{6}The frequency of the event "there are i walkers in a patch with a house at timestep 50" calculated over n simulation runs can be seen as the mean of a sample of n i.i.d. Bernouilli random variables where success denotes that the event occurred and failure denotes that it did not. Thus, the frequency f is the maximum likelihood (unbiased) estimator of the exact probability with which the event occurs. The standard error of the calculated frequency f is the standard deviation of the sample divided by the square root of the sample size. In this particular case, the formula reads:
Std. error (f, n) = (f (1 — f) / (n — 1))^{1/2}
Where f is the frequency of the event, n is the number of samples, and the standard deviation of the sample has been calculated dividing by (n — 1).
^{7}The term 'Markov chain' allows for countably infinite state spaces too (Karr 1990).
^{8}Formally, the occupancy of state i is defined as:
where N_{i}(n) denotes the number of times that the THMC visits state i over the time span {0, 1,…, n}.
^{9}Given that the system has entered the absorbing class C_{v}.
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