The McKinsey Problem-Solving Game (also known as the McKinsey Digital Assessment) is a gamified test designed by Imbellus for McKinsey, assessing the candidate on five dimensions: critical thinking, metacognition, decision-making, situational awareness, and systems thinking. This test has replaced the old, paper-based Problem-Solving Test in almost every McKinsey office.
1.1. The McKinsey Problem-Solving Game is a gamified test
The McKinsey Problem-Solving Game (formerly McKinsey Digital Assessment) is a gamified test designed by the assessment company Imbellus for the consulting firm McKinsey & Company. The new game/test has entered trial since 2017 and has been rolling out extensively in 2020. By January 2021, the Problem-Solving Game had replaced the paper-based PST in virtually every McKinsey office.
In the McKinsey recruitment process, the Problem-Solving Game sits between the resume screening and the case interviews, serving the same purpose as the paper-based tests – ruling out the “unfit” candidates to save time and resources during the expensive case interview phase. The test is mandatory for candidates applying in all practices: General, Operations & Implementation, Research & Analytics, Digital, etc.
Note: As this is a gamified test, in this article, the two terms “game” and “test” will be used interchangeably when referring to PSG.
1.2. The new gamified test is supposedly crack-proof
Now, why did McKinsey change the test format from a paper-based test to a game? Keith McNulty, McKinsey’s Global Director of People Analytics and Measurement, put it this way:
So essentially, McKinsey is trying to create a test/game that is impossible to game (ironic, isn’t it?).
1.3. But in fact it can be broken-down into bite-size pieces
With field reports from hundreds of real test takers, we have gathered enough insights to break down the McKinsey Problem-Solving Game into bite-size pieces, which are fairly consistent across candidates. Using those insights, we can derive working overall approaches to the game.
In this article, we will cover:
- Technical details of the test: time limit, number of tasks and mini-games, assessment criteria
- Break-down of each mini-game: description, underlying logic, and recommended strategy
- Test-taking tips to maximize your chances
- Similar games for practicing the McKinsey Problem Solving Game
2. McKinsey Problem-Solving Game Format
The McKinsey Problem-Solving Game or Digital Assessment has a time limit of 60-80 minutes. The candidate is asked to solve 2 out of 5 possible mini-games. Both the final results and the process are assessed, and if the candidate is found to possess similar skills and tendencies to a McKinsey consultant, they are offered an interview.
For a more detailed guide on the technical details of the game, please check out the McKinsey PSG Starter Guide in my Prospective Candidate Starter Pack.
2.1. Time limit is 71 minutes
As of April 2021, the reported time limit for the McKinsey PST is exactly 71 minutes, with 35 minutes recommended for the first game, and 36 minutes for the second game. Time spent on tutorials is not counted towards the limit.
Ever since the start of the PSG, there have been variations in time limit reports, however these tend to stay between 60-80 minutes. This variation depends on the length of each mini-game.
Actual time allocation depends entirely on the candidate’s decision – however, since the first game is much more predictable, we recommend playing this quickly to allow more time for the second game. With a proper approach, the the first game should take only 15-20 minutes, with time for a double-check taken into account.
Candidates should also make the most out of the tutorial time – try to guess the objective of the mini-game, and think of an overall approach before beginning a mini-game. You can also use that time to make necessary preparations, such as pen and paper, or maybe a light snack to keep yourself energized.
2.2. Each candidate has to solve 2 out of 4 mini-games
As of May 2021, 5 mini-games are confirmed for the McKinsey Problem-Solving Game. The two common mini-games are Ecosystem Building and Plant Defense, the less common ones are Disease Management, Disaster Management, and Migration Management.
The first one is similar to city-building games – except with animals instead of buildings – and the second one is essentially a tower-defense game where you use defensive structure to protect the base from invaders.
Besides the Plant Defense game, three alternatives are found for the second mini-game: Disaster Management and Disease Management.
- Disaster Management involves identifying the natural disaster occurring to an ecosystem and moving the whole system to another location to minimize damage.
- Diseases Management is about identifying an infectious disease, figuring out its rules of infection, and predicting its spread within an ecosystem.
- Migration Management is about directing a group of animals from one point to another such that it loses the least amount of resources and animals.
In most cases, these alternative mini-games are there for beta-testing purposes – the scores from those sessions are not likely to weight heavily in the final decision. As of May 2021, reports of the Disease and Disaster mini-games have been come extremely rare, indicating that they have been phased out; however, the Migration Management mini-game has just come into the scene, so candidates should be prepared for it.
The next parts will be about how candidates are assessed – if that’s not in your interest, you can skip straight to the mini-game and strategy guide using this link.
2.3. Every keystroke and mouse movement will be assessed
Each candidate will be assessed using both product scores (i.e. the final results) and process scores (i.e. how they get those results).
Product scores are determined by your level of success in achieving the objectives of the mini-games. While there is no 100% right answer, some solutions will be better than others. In the first mini-game, you will be given this information through a report screen. For the second mini-game, it would come from the number of turns you survived till the end.
- Mini-game 1: How many species survive?
- Mini-game 2: How many waves of invaders did you fend off?
Process scores, on the other hand, are dictated using data on your patterns during the whole problem-solving process – every keystroke, every click and every mouse movement will be assessed.
The process and product scores are combined to form a profile of problem-solving skills and capabilities. And while there is no official statement from McKinsey about which candidates they select, it is likely that the more you resemble a high-performing consultant at McKinsey, the higher your chances will be.
2.4. Candidates are assessed on five core dimensions
Your problem-solving profile is drawn using the five following dimensions:
- Critical thinking: the ability to form a rational judgment from a set of facts
- Decision-making: the ability to select the best course of action among several options
- Meta-cognition: the ability to use strategies to make learning information and solving problems easier (e.g., testing hypothesis, taking notes)
- Situational awareness: the ability to determine the relationships between different factors and to project the outcomes of a mini-game
- Systems thinking: the ability to understand cause & effect relationships involving several factors and feedback loops (e.g., anticipating several orders of consequences)
The good news is that all the skills assessed are generally not evaluated by themselves, which means training one skill will probably also drive up your assessment scores in others. This is absolutely crucial because you won’t have to go into every nitty-gritty task just to squeeze out some extra score.
Furthermore, while all capabilities must be presented for success, some metrics are considered to be more impactful than others throughout the process. From this Imbellus research paper, we could deduce that Critical Thinking, Situational Awareness, Systems Thinking are the fundamental skills that all successful candidates need to possess. Meanwhile, Decision-Making and Meta-Cognition skills mastery are the advanced skills that will transform candidates from good to great ones.
2.5. The test measure telemetry data to calculate the five dimensions
While it is hard to pinpoint exactly the telemetry data gathered since Imbellus does not fully disclose this information, one way of framing this is by each stage of the problem-solving process itself. Based on our findings from real candidates, we believe the telemetry could be assorted into the following sets, each directly influenced the key activities during the stages from identifying the problem to delivering the next-step recommendation.
Problem Identification: your systematic thinking pattern
- Methodological vs. abstract
- Big-picture thinking vs. detail-oriented
- Example telemetry: prioritization and focus tendency, clicking and decision pattern
Quantitative analysis & data synthesis: ability to translate data into insights
- Drawing relationship between data
- Filter out correlated or irrelevant information
- Example telemetry: data focus pattern, time spent on quantitative task
Hypothesis-crafting: bringing insights into actionable hypothesis
- Putting emphasis on a certain approach / methodology from insights
- Example telemetry: duration of the transition from analysis to decision-making, disrupted status quo period
Decision-making: coherence in actions and thinking
- Random selection or well-thought out decisions based on analysis
- Decisiveness in carrying out actions with the chosen tactics
- Reaction under growing time pressure – panic clicking vs. calm and focus
- Example telemetry: factors connecting each selection, time spent deciding between options
Next-step recommendation: learning and reflection
- Ability to adjust existing strategy and preference for tried-and-true method in presence of new data set or shifting conditions
- Progressive learning and reflection with failures and successes
- Example telemetry: number of click, scrolling speed, time spent on certain data blocks
3.1. Mini-game overview & description
In the Ecosystem Building mini-game, you have to create an ecosystem with 8 species from a list of 40. There are three key objectives: (1) the ecosystem must form a continuous food chain, (2) there must be calorie surplus for every pair of predator and prey (that is, the prey’s production is higher than the predator’s consumption), and (3) the ecosystem must match the terrain specifications of the chosen location.
Here’s a detailed description of data and metrics in the mini-game, and how they relate to the objectives
Data and Metrics
Food Chain Continuity
The candidate is free to switch between choosing location and species during the mini-game. There is also a time bar on the top of the screen.
Old reports indicate that once you’ve submitted your proposed ecosystem, you would receive a score-card in the end showing how it actually plays out. Key measurements might include calories produced and consumed, number of species and individuals alive in the end.
However, recent reports have indicated that results might not be displayed at the end. In either case, it is safe to assume that the underlying principles remain the same.
3.2. Cracking the mini-game
The biggest challenges in the Ecosystem Building mini-game are task prioritization and data processing – most test-takers report that they are overwhelmed by the amount of data given, and do not know how to approach the problem. However, the second problem can be mitigated by reading the rules very carefully, because McKinsey provide specific and detailed instructions in the tutorials.
To overcome both challenges at the same time, first we need to know the “eating rules” (i.e. how species take turns to eat) and then we can develop a 3-step approach to meet those challenges.
EATING RULES AND FEEDING OVERLAPS
In the McKinsey PSG Ecosystem mini-game, species take turns to eat and get eaten, in accordance to very specific and comprehensive rules:
- The species with the highest Calories Provided in the food chain eats first.
- It eats the species with the highest Calories Provided among its prey (if the eating species is a producer, you can assume it automatically bypass this step, as well as steps 3-5).
- The eating species then “consumes” from the eaten species an amount of Calories Provided that is equal to its Calories Needed, which is at the same time substracted an amount equal to the Calories Provided taken from the eaten species.
- If there are two “top prey” species with the same Calories Provided, the eating species will eat from each of them an amount equal to 1/2 of its Calories Needed.
- If the Calories Needed hasn’t been reduced to 0 (i.e.: satisfied), even if the eating species has consumed all the Calories Provided of the first prey the eating species will move on to the next prey with the second-highest Calories Provided, and repeat the above steps; the prey that has been exhausted its Calories Provided will be remove permanently from the food chain and considered extinct.
- After the first species have finished eating, the cycle repeats for the species with the second-highest Calories Provided, then the third-highest, etc. until every species has already eaten. Note: in every step where species are sorted using Calories Provided, it always uses the most recent figure (i.e. the one after consumption by a predator).
- At the end of this process, all species should have new Calories Provided and Calories Needed, both smaller than the original figures. A species survive when its end-game Calorie Needed is equal to 0, and Calorie Provided is higher than 0.
Let’s take a look at an example – try applying the rules above before reading the explanation, and see if you get it right:
Now, here’s how this food chain is resolved:
- The three producers automatically have their Calories Needed satisfied and does not need to eat anything.
- The first species to eat is an animal – the Mouse. It eats equally from Grass and Mushroom, which have equal Calories Provided, an amount of 2,000 each. The Mouse’s Calories Needed reduces to 0, while the Calories Provided for Grass and Mushroom reduce to 3,000 each (Grass and Mushroom survive).
- The second species to eat is the Squirrel. It should have eaten Grass, but Grass’s new Calories Provided is only 3,000, so the Squirrel picks Nuts instead. Squirrel’s Calories Needed becomes 0, while Nuts’ Calories Needed becomes 500 (Nuts survive).
- The third species to eat is the Snake. It eats the Mouse, reducing its own Calories Needed to 0 while taking 2,000 from the 3,000 of the Mouse’s Calories Provided. (Mouse survives)
- The fourth species to eat is the Fox. It eats the Squirrel, reducing its own Calories Needed to 0 while taking 2,000 from the 2,500 of the Squirrel’s Calories Provided. (Squirrel survives)
- The last species to eat is the Tiger. It eats the Snake first, taking away all of the Snake’s 1,500 Calories Provided, then proceeds to take 500 from the Fox’s 1,200, so that its Calories Needed can be reduced to 0 (Snake becomes extinct, Fox survives)
- The Tiger is not eaten by any other animal (Tiger survives).
With these rules in mind, let us go through a 3-step process to building a food chain:
Step 1: Select the location:
- Use a spreadsheet or scratch paper to list the terrain specs and calories provided of the producers of the mini-game.
- Skim through the data to see which terrain specs remain the same across all species, and which ones change. Only changing terrain specs are relevant (there should be 2 of them), the rest are merely “noise” intended to cause information overload.
- Calculate the sum calories provided for the producers of each layer. The layer with the highest calories provided are likely to be the easiest for building the food chain.
Step 2: Build the food chain
- Look through the data to list the consumers with compatible terrain requirements into your spreadsheet.
- Pick the apex predator with the lowest calorie needed as the starting point of the food chain.
- Build the food chain top-down like an issue tree, by listing the food sources of the top predators. Continue drilling down until you reach the “base” level of corals and plants. Ideally the food chain should contain 3-4 levels, and 8 species.
- Alternatively, you can build the food chain in bottom-up manner, by looking at the “Eaten By” specs of each species, until you reach the top predators. Our reports indicate that in real test conditions, this approach can be just as fast as the top-down one.
- During the whole process, try to prioritize species with high calories provided, and low calories needed. This should maximize the chance of calories surplus in the food chain, and leave room for new additions should the first chain do not meet the required 8 species.
- If you finish the chain short of the required 8 species, work bottom-up to find gaps (i.e unused surplus calories), and plug in those gaps with predators or plant-eating animals.
- The whole process should be done on a spreadsheet / scratch paper to facilitate calculations.
Step 3: Triple-check and adjust
- Go back to the beginning of the process and check if every species of your food chain is compatible with the chosen location.
- Make sure the food chain is continuous – that is, the food sources listed fit with the description of each species.
- Check if each species in the food chain is supplied with enough calories and not eaten into extinction using the given eating rules.
- Adjust the food chain if any of the three checks are not met.
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4.1. Mini-game overview & description
The second mini-game of the McKinsey Problem-Solving Game – Plant-Defense – is a turn-based tower-defense game. The candidate is charged with defending a plant at the center of a grid-based map from invading pests, using obstacles and predators, for as long as possible, until the defenses are overwhelmed and the plant is destroyed.
Here’s a detailed description of the gameplay:
- The visual map is divided by a square-grid (size from 10×10 to 12×12), with natural obstacles (called Terrain, or Terrain Transformations) are scattered across the map.
- The game has a recommended time allocation of 12 minutes per stage – which makes 36 minutes in total.
- The game is divided into three maps, each with 2 phases – “planning phase” and “fast-forward phase”.
- The “planning phase” is divided into 3 waves of 5 turns each. Every 3-5 turns, a new stack of Invader appears at the border of the map and starts travelling towards the center base – you have lay out defensive plans to tackle them. The phase lasts until you eliminated all the Invaders / you survive at the end of the 15th turn / your plant is destroyed.
- The “fast-forward phase” comes after the 15th turn of the planning phase. All the remaining Invaders from the planning phase will continue attacking. Your defensive scheme remains unchanged, and unchangeable. Invaders will continuously spawn and attack until the base is destroyed.
- After you’ve finished the game, the number of turns your plant survived will be used as the basis for the product scores.
4.2. Cracking the mini-game
As the Plant Defense mini-game of the McKinsey Problem Solving Game is essentially a tower-defense game, the basic tactics of that game genre can be applied – namely inside-out building and kill-zones. However, as the mini-game locks you from changing placement after a number of turns, contingency planning is also necessary.
I’ll elaborate each of those tactics:
INSIDE-OUT, MULTI-LAYERED DEFENSE
In this tactic, you build multiple layers of defenders outwards from the base, assisted by terrain.
Place your resources close to the plant first. As the inner rings of the map are smaller in circumference, and paths usually converge as you advance towards the center, this helps you maximize the coverage of each resource around the plant early on. In the example below, the “inside-out” approach only takes 8 resources to protect the plant from all directions, while the “outside in” approach takes 24. With this approach, place your most powerful resources closest to the plant, and expand with the less powerful, but longer-range ones.
This isn’t so much of a “tactic”, but a reminder – after 15 turns, you won’t be able to change or place more resources, so try to identify the pattern of the invaders, and quickly adapt your strategy accordingly. It will take a few initial turns to experiment which works best for each type of invader.
Use your resources prudently, create an all-round protection for the plant – lopsided defenses (i.e heavy in one direction, but weak in others) won’t last long – and lasting long is the objective of this mini-game.
5. Alternative Mini-games – Disease & Disaster
Early reports also indicate 2 alternative mini-games as replacements for Plant Defense – called Disaster Management and Disease Management. With the limited information available, it seems these alternatives are rare, but it is best to know what their objectives are and how to deal with them.
5.1. Alternative 1: Disaster Management
In the Disaster Management mini-game of the Problem-Solving Game, the candidate is required to identify the type of natural disaster that has happened to an ecosystem, using limited given information and relocate that ecosystem to ensure/maximize its survivability.
With the two main objectives in mind, here’s how to deal with them:
- Identify the disaster: this is a problem-diagnosis situation – the most effective approach would be to draw an issue tree with each in-game disaster as a branch, skim through data in a bottom-up manner to form a hypothesis, then test that hypothesis by mining all possible data in game (such as wind speed, temperature, etc.)
- Relocate the ecosystem: this is a more complicated version of the location-selection step in the Ecosystem-Building mini-game, with the caveat that you will first have to rule out the locations with specs similar to the ongoing disaster. The rest can be done using a spreadsheet listing the terrain requirements of the species.
Like the Ecosystem Building mini-game, you will solve this mini-game only once, unlike the Plant Defense and the next Disease Management mini-games with multiple maps.
5.2. Alternative 2: Disease Management
In the Disease Management mini-game of the Problem-Solving Game, the candidate is required to identify the infection patterns of a disease within an ecosystem and predict the next individual to be infected The game gives you 3-5 factors for the species (increasing as the game progresses), including name, age, weight, and 3 snapshots of the disease spread (Time 1, Time 2, Time 3) to help you solve the problem.
There is one main objective here only: identify the rules of infection (the second is pretty much straight forward after you know the rules) – and this is another problem-diagnosis situation. The issue tree for this mini-game should have the species factors as branches. Skim through the 3 snapshots to test each branch – once you’re sure which factor underlies and how it correlates with infection, simply choose the predicted individual.
5.3. Alternative 3: Migration Management
The Migration Management mini-game of the PSG is a turn-based puzzle game. The candidate is required to direct the migration of 50 animals. This group carries a certain amount of resources (such as water, food, etc.), often 4-5 resources, each with an amount of 10-30. Every turn, 5 animals die and 5 of each resource is consumed.
It takes 3-5 turns from start to finish for each stage Migration mini-game, and the candidates must place 15 stages in 37 minutes. The candidate must choose among different routes to drive the animals. In each stage, there are points where candidates can collect 3 additional animals or resources (1-3 for each type), and choose to multiply some of the collected resources (1x, 3x and 6x); the game tells the candidate in advance which resources/animals they will get at each point, but not the amount.
The objective is to help the animals arrive at the destination with minimal animal losses, and with specific amounts of resources.
With all of these limited insights in mind, here’s what I recommend for the strategy:
- Nearly every necessary detail is given in advance, so use a scratch paper to draw a table, with the columns being the resources/animals, and the rows being the routes. Quickly calculate the possible ending amount for each resources, assuming you get 2 at every collection point (good mental math will come in handy).
- Choose the route with the highest number of animals, and “just enough” resources to meet requirements.
6. Test-Taking Tips for McKinsey Problem-Solving Game
Besides the usual test-taking tips of “eat, sleep and rest properly before the test”, “tell your friends and family to avoid disturbing”, etc. there are five tips specifically applicable to the Problem-Solving Game I’ve compiled and derived from the reports of test takers:
Tip 1: Don’t think too much about criteria and telemetry measurements
You can’t know for sure which of your actions they are measuring, so don’t try so much to appear “good” before the software that it hurts your performance. One of our interviewers reported that he refrained from double-checking the species information in the Ecosystem Building mini-game for fear of appearing unsure and unplanned.
My advice is to train for yourself a methodical, analytic approach to every problem, so when you do come in for the test, you will naturally appear as such to the software. Once you’ve achieved that, you can forget about the measurements, and focus completely on problem-solving.
Tip 2: Don’t be erratic with in-game actions
While you don’t want to spend half your brain-power trying to “look good” to the software, do avoid erratic behaviors such as randomly selecting between the info panels, or swinging the mouse cursor around when brainstorming (yes, people do that – my Project Manager does the same thing when we do monthly planning for the website).
This kind of behavior might lead the software into thinking that you have unstable or unreliable qualities (again, we can never know for sure, but it’s best to try). One tip to minimize such “bad judgment” is to take your brainstorming outside of the game window, by using a paper, or a spreadsheet.
Tip 3: Always strive for a better solution (Ecosystem Building)
Some of the interviewed test-takers seem to be under a wrong impression that “the end results do not matter as much as the process” – however, for PSG, you need good end results too. This is especially true in the Ecosystem Building, where a “right” answer with no species dying can be easily found with the right strategy.
Consulting culture is highly result-oriented, and this game/test has product scores to reflect that. Having a methodical and analytical approach is not enough – it’s no use being as such if you cannot produce good results (or, “exceptional” results, according to MBB work standards).
Tip 4: Learn to compromise (Plant Defense)
The game is designed so that in many cases, there will be shortcomings in even the best solutions, and your base will never survive. So pay attention to the time limit, and once you’re confident with a “good enough” solution, submit it. Remember, the time limit is shared by all the mini-games and its constituent stages, so spending too much time on one will eat up the necessary time for the next.
Tip 5: Prepare your hardware and Internet properly before the test
While the McKinsey PSG does not require powerful hardware, it is indeed more demanding than the usual recruitment games or tests. A decent computer is highly-advised – the smoother the experience, the more you can focus on problem-solving.
On the other hand, fast Internet connection is a must – in fact, the faster, the better. You don’t want to be disconnected in the middle of the test – so tell other users on your network to avoid using at the same time as the test, and go somewhere with a fast and stable connection if it’s not available at your home.
7.1. Hypothesis-driven problem-solving approach
You may have noticed a lot of the solutions for the mini-game involve an “issue tree” – the centerpiece of the hypothesis-driven problem-solving approach that real consultants use in real projects.
This problem-solving approach is a must for every candidate wishing to apply for consulting – so learn and try to master it by applying it into everyday problems and cases you read on business publications. Practicing case interviews also helps with PSG as well.
You can see the above articles for the important concepts of consulting problem-solving.
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7.2. Mental math and fast reading skills
The McKinsey Problem-Solving Game – especially the 3 ecosystem-related mini-games – require good numerical and verbal aptitude to quickly absorb and analyze the huge amounts of data. Additionally, such skills are also vital to case interviews and real consulting work.
That means a crucial part of PSG practice must include math and reading practice – see the above articles for more details on how to calculate and read 300% faster.
8. Practice with Video Games
Test-takers who regularly play video games, especially strategy games, report a significant advantage from their gaming experience. This is likely due to three main factors:
- The McKinsey PSG is in fact similar in logic and gameplay to a few popular video game genres. The more similar a game is to the PSG, the better it is for practice.
- Video games with data-processing and system management also improve the necessary skills for the PSG.
- Playing video games helps candidates understand how the interface as well as the objective system of a game works – improving their “game sense”.
I am not a fan of video games – in fact, after leaving McKinsey I founded an entertainment startup with the mission to fight the increasing popularity of video games. Yet now I have to tell you to spend a few hours each week playing them to get into McKinsey.
The question is, which games to play? Here’s a list of the games and game genres my team have found to possess many similarities with the McKinsey PSG:
- SimCity series
- Caesar series (Zeus and Poseidon, Caesar III, Emperor ROTK)
- Anno series (Anno 1404, Anno 2070, etc.)
- Cities Skylines
These are very similar in logic to the Ecosystem Building mini-game – you need to balance the production and consumption of buildings and communities, which usually have specific requirements for their locations.
The difference between these and the PSG is that most games are real-time and continuous, meaning you have the opportunity to watch your city develop and correct the mistakes – in the PSG you need to nail it from the start! With that said, the amount of data you need to process in these games will make the McKinsey PSG a walk in the park; the learning curve is not too high either, making these games good practice grounds.
Screenshot from Cities Skylines
Tower defense games
- Kingdom Rush series
- Plants vs Zombies series
Tower-defense games such as Kingdom Rush are near-perfect practice for the Plant Defense mini-game of the McKinsey PSG. Our basic “kill-zone” tactic in fact comes from these games.
Again, there is a caveat when practicing with games – both Plants vs Zombies and Kingdom Rush allow you to correct your mistakes by having the invaders attacking the base multiple times before you lose. Both games also feature fixed and predictable paths of invasion. In the PSG, the path of the invaders change with your actions, and if they reach your base, you’ll lose immediately.
Screenshot from Kingdom Rush 2
Grand strategy and 4X games
- Civilization series
- Europa Universalis series
- Crusader Kings series
Grand strategy and 4X games combines the logic of system-building and tower-defense games (with Civilization being the best example), making them good practice for both games of the PSG. They also require players to manage the largest amount of data among popular game genres (sometimes multiple windows with dozens of stats each).
However, they are also the game with the steepest learning curves – so if you are not one for video games, and/or you don’t have much time before the PSG, these games are not for you. They are also less similar to the PSG on the surface, compared to the two genres above.
Screenshot from Civilization VI