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Transition matrices, state matrices and equilibrium: VCE General Mathematics Unit 4 Matrices

Syllabus dot point

“Construct a transition matrix from a transition diagram or written description, use the matrix recurrence relation S(n+1) = TS(n) to generate a sequence of state matrices, informally identify the equilibrium state of a regular transition matrix, and use S(n+1) = TS(n) + B to model populations with culling and restocking”

16 min read

Quick answer

Columns are "this step", rows are "next step", and every column adds to 1. Step forward with Sn+1=TSnS_{n+1} = TS_n, so Sn=TnS0S_n = T^nS_0; step back with Sn−1=T−1SnS_{n-1} = T^{-1}S_n. The number moving from XX to YY is (number in XX at that step) ×\times (element in row YY, column XX). For a regular TT, large powers settle to an equilibrium state that no longer changes. With culling and restocking use Sn+1=TSn+BS_{n+1} = TS_n + B one step at a time: BB adds (positive) or removes (negative), and the total changes by the sum of BB each step.

Jump to a section
  1. What this dot point is asking
  2. The answer
  3. Exam-style questions
  4. Practice questions

What this dot point is asking

The Study Design's Transition matrices topic has four parts:

  1. use of transition diagrams, their associated transition matrices and state matrices to model transitions between states;
  2. the matrix recurrence relation S0=S_0 = initial state matrix, Sn+1=TSnS_{n+1} = TS_n, to generate a sequence of state matrices;
  3. informal identification of the equilibrium state matrix for regular transition matrices ("no noticeable change from one state matrix to the next");
  4. the recurrence Sn+1=TSn+BS_{n+1} = TS_n + B to model populations with culling and restocking.

This is the most heavily examined part of the Matrices module. In 2025 Examination 2, Questions 12 and 13 (6 of the 12 Matrices marks) were transition matrices, and the report statistics are sobering: 13%13\% for a restocking total and 9%9\% for a Week 10 percentage. In 2023 Examination 1, Questions 27 and 32 were the two hardest matrix questions (39%39\% and 18%18\%). The skills are not hard; the marks are lost to reading the question: which step, which states count, what percentage of what.

(Leslie matrices use the same recurrence with a different kind of matrix and have their own page.)

The answer

Transition diagrams and transition matrices

A transition diagram shows states as circles and the proportion moving between them each step as labelled arrows; a loop is the proportion that stays. Everything in a state goes somewhere, so the proportions leaving each state (including its loop) add to 1.

Transition diagram for commuters choosing car, public transport or bikeThree states: Car, Public transport and Bike. From Car: 0.80 stay, 0.15 move to Public transport, 0.05 move to Bike. From Public transport: 0.85 stay, 0.10 move to Car, 0.05 move to Bike. From Bike: 0.70 stay, 0.10 move to Car, 0.20 move to Public transport. The proportions leaving each state, including its loop, add to 1.0.800.150.050.100.850.050.100.200.70CarPublicBikeArrows: proportions who switch each week. Loops (bold): proportions who stay.

The matching transition matrix puts "this week" across the top (columns, where people are from) and "next week" down the side (rows, where they go to):

T=[0.800.100.100.150.850.200.050.050.70]columns: C,P,B this weekrows: C,P,B next weekT = \begin{bmatrix} 0.80 & 0.10 & 0.10 \\ 0.15 & 0.85 & 0.20 \\ 0.05 & 0.05 & 0.70 \end{bmatrix} \qquad \begin{aligned} &\text{columns: } C, P, B \text{ this week} \\ &\text{rows: } C, P, B \text{ next week} \end{aligned}

Reading elements:

  • tijt_{ij} is the proportion of those in state jj this step who are in state ii next step. So t21=0.15t_{21} = 0.15: 15%15\% of car commuters switch to public transport.
  • The leading diagonal is the proportion who stay. In 2025 Examination 2 Question 13a it was all zeros, which meant nobody repeats an activity in consecutive weeks.
  • Each column sums to 1. A missing element is found from this, which is how an incomplete diagram is completed (2023 Examination 1 Question 32).
  • A column like [010]T\begin{bmatrix} 0 & 1 & 0 \end{bmatrix}^T (a 1 on the diagonal) is an absorbing state: once in, never out.

State matrices and stepping forward

A state matrix is a column matrix of how many (or what proportion) are in each state at one step. With initial state S0S_0:

Sn+1=TSn⟹Sn=TnS0.S_{n+1} = TS_n \quad\Longrightarrow\quad S_n = T^nS_0.

The transition matrix goes on the left. Because each column of TT sums to 1, the total is conserved from step to step.

Two counting details cost the most marks:

  • Which power? If Week 1 is S1S_1, then Week 10 is S10=T9S1S_{10} = T^9S_1: nine steps, not ten. Count the steps between the two states.
  • Do not round the state. Expected numbers can be decimals (7.776 children). Keep the full CAS values and round only the final answer; in 2025 only 9%9\% got the Week 10 percentage right, many after rounding a state to a whole number part-way.

Counting how many move between two states

The number moving from XX to YY in one step is

(number in X at that step)×(element in row Y, column X).(\text{number in } X \text{ at that step}) \times (\text{element in row } Y, \text{ column } X).

So first find the state at the right step, then multiply by one element. In 2025 Examination 2 Question 13b(ii), 7.7767.776 children were in gardening in Week 3 and k32=0.24k_{32} = 0.24 move to music, giving 7.776×0.24≈27.776 \times 0.24 \approx 2. A "percentage of" question then divides by whichever total the question names: in 2023 Examination 1 Question 32 the 42 students who stayed on the oval were a percentage of Wednesday's 127, not Tuesday's 140.

Stepping backwards

If you know SnS_n and want the state one step earlier, undo the multiplication with the inverse:

Sn−1=T−1Sn.S_{n-1} = T^{-1}S_n.

This was 2025 Examination 2 Question 12a (53%53\%). Check by multiplying your answer by TT.

Regular matrices and the equilibrium state

A transition matrix is regular if some power of it has all elements positive. For a regular TT, the state matrices settle down: after enough steps there is no noticeable change from one state to the next. This is the equilibrium (steady) state. The Study Design asks for it informally, so the standard method is:

  1. compute TnS0T^nS_0 for a large nn (say 50) and for n+1n + 1;
  2. if they agree to the accuracy needed, that is the equilibrium.

For the commuters with S0=[600300100]TS_0 = \begin{bmatrix} 600 & 300 & 100 \end{bmatrix}^T, T50S0≈[333.3523.8142.9]TT^{50}S_0 \approx \begin{bmatrix} 333.3 & 523.8 & 142.9 \end{bmatrix}^T, and it would be the same from any starting split of the same 1000 people. The equilibrium proportions do not depend on S0S_0; the equilibrium numbers depend only on the total. At equilibrium, TS=STS = S.

A matrix with an absorbing state (a 1 on the diagonal and 0s elsewhere in that column) is not regular. Everything drains into the absorbing state over time, as in 2023 Examination 1 Question 27 where all the birds ended up at NN.

Culling and restocking

When a fixed number is added (restocking, new enrolments) or removed (culling, catching) each step, the model becomes

Sn+1=TSn+B,S_{n+1} = TS_n + B,

where BB is a column matrix: positive elements add to a state, negative elements remove from it. Two consequences:

  • The total is no longer conserved: it changes by the sum of the elements of BB every step.
  • Sn=TnS0S_n = T^nS_0 no longer works. Step one period at a time (on CAS, define the rule and repeat, or use a recursion).

The model can also be designed to keep something constant: in 2023 Examination 2 Question 11, 5%5\% of 180 workers (9 people) left each show, so B=[90]TB = \begin{bmatrix} 9 & 0 \end{bmatrix}^T kept the workforce at 180.

When a state such as "left" absorbs people, read carefully which states the question counts. In 2025 Examination 2 Question 12b, "enrolled" meant NN, TT and PP only: 50 children, not 115.

Worked examples: building, stepping, equilibrium and restocking

Two steps and a movement count

Use the commuter matrix TT above with S0=[600300100]S_0 = \begin{bmatrix} 600 \\ 300 \\ 100 \end{bmatrix} (CC, PP, BB).

Step 1: one week
S1=TS0=[0.80(600)+0.10(300)+0.10(100)0.15(600)+0.85(300)+0.20(100)0.05(600)+0.05(300)+0.70(100)]=[520365115]S_1 = TS_0 = \begin{bmatrix} 0.80(600) + 0.10(300) + 0.10(100) \\ 0.15(600) + 0.85(300) + 0.20(100) \\ 0.05(600) + 0.05(300) + 0.70(100) \end{bmatrix} = \begin{bmatrix} 520 \\ 365 \\ 115 \end{bmatrix}.
Step 2: two weeks
S2=TS1=[464411.25124.75]S_2 = TS_1 = \begin{bmatrix} 464 \\ 411.25 \\ 124.75 \end{bmatrix}. The total is still 1000.
Step 3: how many switch from car to public transport between week 1 and week 2
In week 1 there are 520 car commuters, and tPC=0.15t_{PC} = 0.15: 520×0.15=78520 \times 0.15 = 78 people.

Marker's note: the movement count uses the state at the start of that step (S1S_1), not S0S_0 or S2S_2.

The equilibrium state

Find the long-run number of commuters using each mode.

Step 1: large powers
T40S0=[333.3335523.8094142.8571]T^{40}S_0 = \begin{bmatrix} 333.3335 \\ 523.8094 \\ 142.8571 \end{bmatrix} and T50S0=[333.3333523.8095142.8571]T^{50}S_0 = \begin{bmatrix} 333.3333 \\ 523.8095 \\ 142.8571 \end{bmatrix}.
Step 2: check there is no noticeable change
T51S0T^{51}S_0 gives the same values, so this is the equilibrium.
Answer
In the long run about 333 people drive, 524 use public transport and 143 cycle. Starting instead from [001000]T\begin{bmatrix} 0 & 0 & 1000 \end{bmatrix}^T gives the same answer, because TT is regular.

Stepping back one week

This week's state is S0=[600300100]TS_0 = \begin{bmatrix} 600 & 300 & 100 \end{bmatrix}^T. Find last week's state.

Step 1: rearrange
S0=TS−1S_0 = TS_{-1}, so S−1=T−1S0S_{-1} = T^{-1}S_0.
Step 2: CAS
T−1S0=[714.29208.7976.92]T^{-1}S_0 = \begin{bmatrix} 714.29 \\ 208.79 \\ 76.92 \end{bmatrix}.
Answer
About 714 drove, 209 used public transport and 77 cycled last week. Check: T[714.29208.7976.92]T≈[600300100]TT\begin{bmatrix} 714.29 & 208.79 & 76.92 \end{bmatrix}^T \approx \begin{bmatrix} 600 & 300 & 100 \end{bmatrix}^T.

Culling and restocking in two regions

Kangaroos live in North (NN) and South (SS) regions of a reserve. Each month 90%90\% of North kangaroos stay and 10%10\% move south; 20%20\% of South kangaroos move north and 80%80\% stay. There are 300 in each region. Each month rangers remove 30 from North and release 10 into South.

Step 1: set up
T=[0.90.20.10.8]T = \begin{bmatrix} 0.9 & 0.2 \\ 0.1 & 0.8 \end{bmatrix}, B=[−3010]B = \begin{bmatrix} -30 \\ 10 \end{bmatrix}, S0=[300300]S_0 = \begin{bmatrix} 300 \\ 300 \end{bmatrix}.
Step 2: one month
S1=[270+6030+240]+[−3010]=[300280]S_1 = \begin{bmatrix} 270 + 60 \\ 30 + 240 \end{bmatrix} + \begin{bmatrix} -30 \\ 10 \end{bmatrix} = \begin{bmatrix} 300 \\ 280 \end{bmatrix}.
Step 3: two months
S2=[270+5630+224]+[−3010]=[296264]S_2 = \begin{bmatrix} 270 + 56 \\ 30 + 224 \end{bmatrix} + \begin{bmatrix} -30 \\ 10 \end{bmatrix} = \begin{bmatrix} 296 \\ 264 \end{bmatrix}.
Answer
After two months there are 296 in North and 264 in South. The total falls from 600 to 580 to 560: by −30+10=−20-30 + 10 = -20 each month, exactly the sum of BB.
Common traps
Building the matrix with rows as "from"
VCAA transition matrices have columns as "this step" and each column adds to 1. Check the column sums before anything else.
Using the wrong power
From S1S_1 to Week 10 is T9T^9. Count steps, not labels.
Rounding a state part-way
Keep 7.776 as 7.776. Round only the final answer, to the accuracy asked.
Using the wrong state for a movement count
The number moving from XX to YY uses how many are in XX at the start of that step.
Percentage of the wrong total
Read "of the students on the oval on Wednesday" literally and divide by Wednesday's number.
Counting absorbed states in a total
"Enrolled", "working" or "alive" usually excludes the "left" or "dead" state.
Using TnS0T^nS_0 with a BB matrix
With culling or restocking, step one period at a time.

Calling a non-regular matrix's long-run state an equilibrium spread across all states. An absorbing state collects everything.

Exam technique

Before any calculation, write the column sums of TT (all 1) and label the rows and columns with the states. For every "after nn weeks" question, write the rule you are using, such as S10=K9S1S_{10} = K^9S_1, because that line earns method marks on 2-mark questions. For an equilibrium, write the two large powers you compared. For a restocking question, write Sn+1=TSn+BS_{n+1} = TS_n + B with the actual matrices, then list each state you compute.

Note

A transition matrix is a table of "where do people go next". Each column describes one group now, and splits it up by where those people will be next time: the numbers in a column add to 1 because everyone ends up somewhere. Multiply the table by the current numbers to get the next numbers, and keep going to see the future. Many systems settle down so that the numbers stop changing: that is the equilibrium. If people are also added or removed each time, add that change after every step.

Exam-style questions

Questions in the style of VCAA exam questions on this dot point, each with a worked answer. They are written by ExamExplained unless tagged "Past paper"; the year shows the paper a question is modelled on.

2025 VCAA-style4 marks
27 children do one of cooking (CC), gardening (GG) or music (MM) each week for 10 weeks. The transition matrix (columns this week, rows next week, in the order CC, GG, MM) is K=[00.760.360.5500.640.450.240]K = \begin{bmatrix} 0 & 0.76 & 0.36 \\ 0.55 & 0 & 0.64 \\ 0.45 & 0.24 & 0 \end{bmatrix}. (a) What do the values on the leading diagonal of KK indicate? (b) In Week 1 all 27 children do cooking. (i) Calculate the expected percentage of children doing cooking in Week 10, correct to one decimal place. (ii) Find the expected number of children who do gardening in Week 3 and then move to music in Week 4, to the nearest whole number.
Show worked answer →

(a) The leading diagonal holds the proportions who stay in the same activity, and here every one is 00: no child does the same activity in two consecutive weeks. (1 mark; 67%67\%.)

(b)(i) Week 1 is S1=[2700]S_1 = \begin{bmatrix} 27 \\ 0 \\ 0 \end{bmatrix}, so Week 10 is nine steps later: S10=K9S1=[10.0856…9.9648…6.9495…]S_{10} = K^9 S_1 = \begin{bmatrix} 10.0856\ldots \\ 9.9648\ldots \\ 6.9495\ldots \end{bmatrix}. Percentage cooking =10.085627×100=37.4%= \frac{10.0856}{27} \times 100 = 37.4\%. (1 mark; only 9%9\%. Rounding 10.0856 to 10 before dividing gives 37.0%37.0\%; the examiners stressed rounding only at the final step.)

(b)(ii) Week 3 is S3=K2S1=[15.667.7763.564]S_3 = K^2 S_1 = \begin{bmatrix} 15.66 \\ 7.776 \\ 3.564 \end{bmatrix}, so 7.776 children are expected to do gardening. The proportion moving from GG to MM is k32=0.24k_{32} = 0.24: 7.776×0.24=1.866≈27.776 \times 0.24 = 1.866 \approx 2 children. (2 marks; 17%17\% full marks.)

Source: VCAA 2025 General Mathematics Examination 2, Question 13, and the 2025 examination report.

2025 VCAA-style2 marks
Children at an early learning centre are in Nursery (NN), Toddler (TT) or Pre-kinder (PP), or have left (LL). With columns this year and rows next year, M=[0.250000.6250.250000.6250.100.1250.1250.91]M = \begin{bmatrix} 0.25 & 0 & 0 & 0 \\ 0.625 & 0.25 & 0 & 0 \\ 0 & 0.625 & 0.1 & 0 \\ 0.125 & 0.125 & 0.9 & 1 \end{bmatrix} and Sn+1=MSnS_{n+1} = MS_n. At the start of 2024, S2024=[4151527]S_{2024} = \begin{bmatrix} 4 \\ 15 \\ 15 \\ 27 \end{bmatrix}. (a) Find S2023S_{2023}. (b) From 2025, new children enrol at the start of each year, and Sn+1=MSn+BS_{n+1} = MS_n + B with B=[125100]B = \begin{bmatrix} 12 \\ 5 \\ 10 \\ 0 \end{bmatrix}. Find the expected total number of children enrolled at the centre at the start of 2026, to the nearest whole number.
Show worked answer →

(a) S2024=MS2023S_{2024} = MS_{2023}, so step backwards with the inverse: S2023=M−1S2024=[1620250]S_{2023} = M^{-1}S_{2024} = \begin{bmatrix} 16 \\ 20 \\ 25 \\ 0 \end{bmatrix}. (1 mark; 53%53\%.) Check: M[1620250]T=[4151527]TM \begin{bmatrix} 16 & 20 & 25 & 0 \end{bmatrix}^T = \begin{bmatrix} 4 & 15 & 15 & 27 \end{bmatrix}^T.

(b) S2025=MS2024+B=[1311.2520.87542.875]S_{2025} = MS_{2024} + B = \begin{bmatrix} 13 \\ 11.25 \\ 20.875 \\ 42.875 \end{bmatrix} and S2026=MS2025+B=[15.2515.937519.118864.6938]S_{2026} = MS_{2025} + B = \begin{bmatrix} 15.25 \\ 15.9375 \\ 19.1188 \\ 64.6938 \end{bmatrix}.

Children enrolled are those in NN, TT and PP: 15.25+15.94+19.12=50.315.25 + 15.94 + 19.12 = 50.3, so 50 children. (1 mark; only 13%13\%. Many students answered 115 by also counting the children who had left.)

Source: VCAA 2025 General Mathematics Examination 2, Question 12, and the 2025 examination report.

2023 VCAA-style2 marks
A circus needs 180 workers for each show. From one show to the next, workers either continue working (WW) or leave the circus (LL), and those who leave do not return. It is known that 95%95\% of workers continue working. The situation is modelled by S0=[1800]S_0 = \begin{bmatrix} 180 \\ 0 \end{bmatrix}, Sn+1=TSn+BS_{n+1} = TS_n + B. (a) Write down the transition matrix TT (columns this show, rows next show, order WW, LL). (b) Write down matrix BB so that the circus always has 180 workers.
Show worked answer →

(a) From WW: 0.950.95 stay, 0.050.05 leave. From LL: everyone stays left (they never return), so that column is 00 and 11.

T=[0.9500.051]T = \begin{bmatrix} 0.95 & 0 \\ 0.05 & 1 \end{bmatrix}

(1 mark; 34%34\%. 0.50.5 was often written for 0.050.05, and the 11 was left out or put in the first row.)

(b) Each show 0.05×180=90.05 \times 180 = 9 workers leave, so 9 new workers must be added to WW: B=[90]B = \begin{bmatrix} 9 \\ 0 \end{bmatrix}. (1 mark; 39%39\%. The examiners also accepted [9−9]\begin{bmatrix} 9 \\ -9 \end{bmatrix}.) Check: TS0+B=[171+99+0]=[1809]TS_0 + B = \begin{bmatrix} 171 + 9 \\ 9 + 0 \end{bmatrix} = \begin{bmatrix} 180 \\ 9 \end{bmatrix}.

Source: VCAA 2023 General Mathematics Examination 2, Question 11, and the 2023 examination report.

2023 VCAA-style1 mark
The transition matrix TT models the movement of a species of bird between locations MM, NN and OO from one day to the next (columns this day, rows next day, order MM, NN, OO): T=[1309101311101300]T = \begin{bmatrix} \tfrac{1}{3} & 0 & \tfrac{9}{10} \\ \tfrac{1}{3} & 1 & \tfrac{1}{10} \\ \tfrac{1}{3} & 0 & 0 \end{bmatrix}. Which statement best describes the long term? A. No birds will remain at location MM. B. No birds will remain at location NN. C. All of the birds will end up at location MM. D. All of the birds will end up at location OO. E. An equal number of birds will be at all three locations.
Show worked answer →

Column NN is [010]\begin{bmatrix} 0 \\ 1 \\ 0 \end{bmatrix}: every bird at NN stays at NN forever. Birds at MM and OO keep leaking to NN (13\frac{1}{3} of MM and 110\frac{1}{10} of OO each day) and none ever come back, so in the long term all birds end up at NN and none remain at MM or OO. Of the options, only A is true. (A large power of TT, such as T60T^{60}, has row NN equal to 1  1  11\;1\;1.)

Only 39%39\% answered correctly. TT is not regular (its powers always keep the zeros in column NN), which is why the long-term state does not depend on sharing between all three locations.

Source: VCAA 2023 General Mathematics Examination 1, Question 27, and the 2023 examination report.

2023 VCAA-style1 mark
Students spend lunch at the playground (PP), basketball courts (BB), oval (OO) or library (LL). Once the loops are completed (each column sums to 1), the transition diagram gives (columns this day, rows next day, order PP, BB, OO, LL) T=[0.20.40.30.40.30.30.30.20.40.20.30.10.10.10.10.3]T = \begin{bmatrix} 0.2 & 0.4 & 0.3 & 0.4 \\ 0.3 & 0.3 & 0.3 & 0.2 \\ 0.4 & 0.2 & 0.3 & 0.1 \\ 0.1 & 0.1 & 0.1 & 0.3 \end{bmatrix}. On Monday 150, 50, 220 and 40 students were at PP, BB, OO and LL. Of the students expected on the oval on Wednesday, the percentage who were also on the oval on Tuesday is closest to A. 27% B. 30% C. 33% D. 47% E. 52%
Show worked answer →

STue=TSMon=[13213414054]S_{\text{Tue}} = TS_{\text{Mon}} = \begin{bmatrix} 132 \\ 134 \\ 140 \\ 54 \end{bmatrix} and SWed=TSTue=[143.6132.612756.8]S_{\text{Wed}} = TS_{\text{Tue}} = \begin{bmatrix} 143.6 \\ 132.6 \\ 127 \\ 56.8 \end{bmatrix}.

Of the 140 on the oval on Tuesday, 0.3×140=420.3 \times 140 = 42 stay on the oval on Wednesday. As a percentage of Wednesday's 127 oval students: 42127×100=33%\frac{42}{127} \times 100 = 33\%, option C.

Only 18%18\% answered correctly; 54%54\% chose B, 30%30\%, by quoting the proportion t33=0.3t_{33} = 0.3 who stay on the oval. That is a percentage of Tuesday's oval students, but the question asks for a percentage of Wednesday's.

Source: VCAA 2023 General Mathematics Examination 1, Question 32, and the 2023 examination report.

Practice questions

Original practice questions graded from foundation to exam level, each with a full worked solution. Try them before revealing the solution.

foundation2 marks
Each month, members of a gym either stay members (MM) or become non-members (NN). Of the members, 70%70\% are still members next month. Of the non-members, 40%40\% join the gym the next month. (a) Write the transition matrix TT with columns "this month" and rows "next month" in the order MM, NN. (b) State what the element t21t_{21} represents.
Show worked solution →

(a) Column MM (from members): 0.70.7 stay, 0.30.3 leave. Column NN (from non-members): 0.40.4 join, 0.60.6 stay out.

T=[0.70.40.30.6]T = \begin{bmatrix} 0.7 & 0.4 \\ 0.3 & 0.6 \end{bmatrix}

Each column sums to 1. (1 mark)

(b) t21t_{21} is row 2 (NN next month), column 1 (MM this month): the proportion (30%30\%) of this month's members who are not members next month. (1 mark)

foundation2 marks
For the gym in the previous question, 500 of 2000 people are members this month, so S0=[5001500]S_0 = \begin{bmatrix} 500 \\ 1500 \end{bmatrix}. Find S1S_1 and S2S_2.
Show worked solution →

S1=TS0=[0.7(500)+0.4(1500)0.3(500)+0.6(1500)]=[9501050]S_1 = TS_0 = \begin{bmatrix} 0.7(500) + 0.4(1500) \\ 0.3(500) + 0.6(1500) \end{bmatrix} = \begin{bmatrix} 950 \\ 1050 \end{bmatrix}

(1 mark)
S2=TS1=[0.7(950)+0.4(1050)0.3(950)+0.6(1050)]=[1085915]S_2 = TS_1 = \begin{bmatrix} 0.7(950) + 0.4(1050) \\ 0.3(950) + 0.6(1050) \end{bmatrix} = \begin{bmatrix} 1085 \\ 915 \end{bmatrix}

(1 mark) The total stays at 2000 because each column of TT sums to 1.

core3 marks
For the same gym, (a) use large powers of TT to find the long-run number of members, to the nearest whole number. (b) Explain why the answer would be the same if everybody started as a non-member. (c) Show that the equilibrium state satisfies TS=STS = S.
Show worked solution →

(a) T50S0=[1142.857…857.142…]T^{50}S_0 = \begin{bmatrix} 1142.857\ldots \\ 857.142\ldots \end{bmatrix}, and T51S0T^{51}S_0 is the same, so in the long run about 1143 members (and 857 non-members). (1 mark)

(b) TT is regular (all its elements are already positive), so the state settles to the same equilibrium from any starting state with the same total, 2000. Only the total matters. (1 mark)

(c) The exact equilibrium is 47\frac{4}{7} and 37\frac{3}{7} of 2000, since 0.3M=0.4N0.3M = 0.4N (as many leave as join). T[8000/76000/7]=[0.7(8000/7)+0.4(6000/7)0.3(8000/7)+0.6(6000/7)]=[8000/76000/7]T\begin{bmatrix} 8000/7 \\ 6000/7 \end{bmatrix} = \begin{bmatrix} 0.7(8000/7) + 0.4(6000/7) \\ 0.3(8000/7) + 0.6(6000/7) \end{bmatrix} = \begin{bmatrix} 8000/7 \\ 6000/7 \end{bmatrix}, unchanged. (1 mark)

core2 marks
For the same gym, the state this month is [8001200]\begin{bmatrix} 800 \\ 1200 \end{bmatrix}. Find the state last month and interpret it.
Show worked solution →

Sthis=TSlastS_{\text{this}} = TS_{\text{last}}, so Slast=T−1[8001200]S_{\text{last}} = T^{-1}\begin{bmatrix} 800 \\ 1200 \end{bmatrix}. det⁡(T)=0.42−0.12=0.3\det(T) = 0.42 - 0.12 = 0.3 and T−1=10.3[0.6−0.4−0.30.7]T^{-1} = \frac{1}{0.3}\begin{bmatrix} 0.6 & -0.4 \\ -0.3 & 0.7 \end{bmatrix}. (1 mark)

Slast=10.3[0.6(800)−0.4(1200)−0.3(800)+0.7(1200)]=10.3[0600]=[02000]S_{\text{last}} = \frac{1}{0.3}\begin{bmatrix} 0.6(800) - 0.4(1200) \\ -0.3(800) + 0.7(1200) \end{bmatrix} = \frac{1}{0.3}\begin{bmatrix} 0 \\ 600 \end{bmatrix} = \begin{bmatrix} 0 \\ 2000 \end{bmatrix}.

Last month the gym had no members: all 2000 people were non-members, and 40%40\% of them (800) joined. (1 mark)

exam3 marks
Fish live in two connected lakes, XX and YY. Each year 85%85\% of the fish in XX stay and 15%15\% move to YY; 5%5\% of the fish in YY move to XX and 95%95\% stay. There are 2000 fish in XX and 3000 in YY. Each year 200 fish are stocked into XX and 100 are caught (removed) from YY. (a) Write the recurrence relation. (b) Find the number of fish in each lake after 2 years. (c) By how much does the total population change each year, and why?
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(a) S0=[20003000]S_0 = \begin{bmatrix} 2000 \\ 3000 \end{bmatrix}, Sn+1=[0.850.050.150.95]Sn+[200−100]S_{n+1} = \begin{bmatrix} 0.85 & 0.05 \\ 0.15 & 0.95 \end{bmatrix}S_n + \begin{bmatrix} 200 \\ -100 \end{bmatrix}. (1 mark)

(b) S1=[1700+150+200300+2850−100]=[20503050]S_1 = \begin{bmatrix} 1700 + 150 + 200 \\ 300 + 2850 - 100 \end{bmatrix} = \begin{bmatrix} 2050 \\ 3050 \end{bmatrix}, then S2=[1742.5+152.5+200307.5+2897.5−100]=[20953105]S_2 = \begin{bmatrix} 1742.5 + 152.5 + 200 \\ 307.5 + 2897.5 - 100 \end{bmatrix} = \begin{bmatrix} 2095 \\ 3105 \end{bmatrix}. (1 mark)

(c) The total rises by 100 fish each year (50005000, 51005100, 52005200). The transition matrix conserves the total because its columns sum to 1, so only BB changes it: 200−100=100200 - 100 = 100. (1 mark)

exam2 marks
A transition matrix is T=[0.600.41]T = \begin{bmatrix} 0.6 & 0 \\ 0.4 & 1 \end{bmatrix} with states AA and BB. (a) Is TT regular? Explain. (b) Describe the long-term state of the system.
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(a) No. Every power of TT keeps a 00 in row 1, column 2 (anything in BB stays in BB), so no power of TT has all positive elements. (1 mark)

(b) BB is an absorbing state: each step 40%40\% of whatever is in AA moves to BB and nothing returns. In the long run everything ends up in BB (TnT^n approaches [0011]\begin{bmatrix} 0 & 0 \\ 1 & 1 \end{bmatrix}). (1 mark)

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