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. What is McKinsey Problem-Solving Game?

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:

“Recruiting only knows if candidates got the right answer, not how they approached the question. Plus, there’s a large amount of strategy, preparation, and luck involved in multiple-choice tests, and if you use them in the selection process, it reinforces the status quo—at a time when you are looking to widen the scope of candidates you’re hiring.”

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 scenarios, assessment criteria
  • Break-down of each scenario: 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 4 possible scenarios. 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 60-80 minutes

The average time limit for the whole test is around 60-80 minutes, although the exact number of parts and time limit varies between offices and candidates. During the test, you are freely allowed to allocate the time for each task. Time spent on tutorials is not counted towards the limit.

For the candidate, this means making the most out of the tutorial time – try to guess the objective of the scenario, and think of an overall approach before beginning a scenario. Also, move on immediately to the next scenario after you’re confident with the first one – that would buy you some time

2.2. Each candidate has to solve 2 out of 4 scenarios

As of February 2021, 4 scenarios are confirmed for the McKinsey Problem-Solving Game. The two common scenarios are Ecosystem Building and Plant Defense. 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, two alternatives are found for the second scenario: Disaster Management and Disease Management. Disaster Management involves identifying the natural disaster occuring to an ecosystem and moving the whole system to another location to minimize damage, while the Diseases Management scenario is about identifying an infectious disease and predicting its spread within an ecosystem.

In some cases, due to ongoing development, a third scenario will be added to your test, mostly for beta-testing purposes – the scores from those sessions will not be counted towards the final decision.

The next parts will be about how candidates are assessed – if that’s not in your interest, you can skip straight to the scenario 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 scenarios. While there is no 100% right answer, some solutions will be better than others. In the first scenario, you will be given this information through a report screen. For the second scenario, it would come from the number of turns you survived till the end.

  • Scenario 1: How many species survive? How much energy do they provide and consume?
  • Scenario 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 scenario
  • 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.

3. Breaking Down the Test – Ecosystem-Building

3.1. Scenario overview & description

In the Ecosystem Building scenario, 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) the calorie production and consumption of the food chain must be balanced, and (3) the ecosystem must match the terrain specifications of the chosen location.

Here’s a detailed description of data and metrics in the scenario, and how they relate to the objectives 


Data and Metrics

Terrain Match

  • There are two “worlds” on which you must build the ecosystem: the Coral Reef and the Mountain
  • Each location in the Mountain world has the 7 following specifications: altitude, cloud height, soil pH level, wind speed, rainfall, sunlight, moisture
  • Each location in the Coral Reef has the 7 following specifications: depth, current, clarity, temperature, salinity, oxygen level and wind speed.
  • Terrain specifications are inter-correlated.
  • Each species also has a few required terrain specifications – if these terrain requirements are not met, the species will die out. These requirements are often not exact numbers, but ranges (e.g: Temperature: 20 to 30 C)

Food Chain Continuity

  • The species are divided into plants/corals, herbivores (animals eating plants and corals), and predators (animals eating other animals).
  • Each species has a few natural predators (Eaten By), and prey (Food Sources). The exceptions are producers (i.e plants that convert sunlight to energy) and top predators.

Energy Balance

  • Each species will be placed on a group basis, with the number of individuals in each group ranging from 20 to 60.
  • Each species has a calorie consumption and a calorie production figure. These are “per individual”, so you have to perform the math to get the true consumption and production figures of the whole species.

The candidate is free to switch between choosing location and species during the scenario. There is also a time bar on the top of the screen (no clock), but this bar is for the time limit of the whole test – so watch out, don’t spend all the time on this first scenario

After you submit your proposed ecosystem, you will receive a score-card in the end showing how it actually plays out. Key measurements include calories produced and consumed, number of species and individuals alive in the end.

3.2. Cracking the scenario

The biggest challenges in the Ecosystem Building scenario 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. McKinsey does not provide specific instructions in the test – the tutorial is mostly on in-game navigation.

To overcome both challenges at the same time, use the following 3-step process:


“Prey overlap” occurs when two predators feed on the same prey. The rule of the game is the more consuming predators, and the more productive prey are prioritized. When prey overlap occurs, use this rule to check if the system works.

For example, in this case, the Sharks die out because the Orcas, prioritizing the most productive Seals, accidentally eats all of their food before Sharks can get their hands (or actually, teeth) on it. One possible solution is to take out the Sharks, and replace them with another predator feeding on Fish or Sea Lions (avoiding conflict with Orcas, which already got their bellies full eating Seals)

4. Breaking Down the Test – Plant-Defense

4.1. Scenario overview & description

The second scenario 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. The scenario plays out until the time limit for the test is reached, or when the defenses are overwhelmed and the plant is destroyed.

Here’s a detailed description of the gameplay:

  • The scenario is divided into three maps, each with at least 15 turns, themselves divided into 3 phases of 5 turns.
  • The map is divided by a square-grid, and natural obstacles are scattered across the map.
  • The scenario has no explicit time-limit – its time limit is what remains after you finish the first scenario (Ecosystem Building)
  • After you’ve finished the scenario, the number of turns your plant survived will be used as the basis for the product scores.
  • The basis of this scenario revolves around using Resources to push back Invaders – as seen in the table below:




  • At the beginning of each phase, you are allowed to choose and place 5 resources – divided into defenders (which “kill” the invaders, such as wolves, eagles, or snakes) and terrains (which slow down the invaders, such as mountains, bushes, and rocks)
  • After each turn, one resource will be activated and locked – meaning you cannot change or remove that resource placement. The rest can be altered to adapt with the circumstances.
  • Each resource has a “sphere of influence” – once an invader steps into the sphere, the resource will take effect on them (damage/slow down)
  • The level of effect (damage/blocking) and the sphere size vary between each resource type – but in general the more powerful they are, the smaller the sphere of influence.
  • Each resource has their own limitations in placement (e.g: resource X can only be placed near Y).
  • Each resource is effective for certain types of invaders


  • At every turn, new invaders will appear in stacks of 20-100 (informed on the map by an icon indicating which directions they come from) and move one step closer to your plant.
  • If they reach the plant, they will start doing damage to the plant. The amount of damage varies, depending on stack size and type of invaders.
  • The number of invaders will keep increasing until they overwhelm your defenses, and the plant is destroyed.
  • Each invader is countered by certain types of resources.

4.2. Cracking the scenario

As the Plant Defense scenario 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 scenario locks you from changing placement after a number of turns, contingency planning is also necessary.

I’ll elaborate each of those tactics:

Inside-out building: 

“Inside-out building” is when you place your resources close to the plant first, then expand outward. 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.

Kill Zones:

Kill-zones” are an actual building tactic of real-life fortifications, combining obstacles and overlapping firepower to stop invaders dead (literally) in their tracks. The same applies in tower-defense games, such as the Plant Defense scenario. 

Using this tactic in the McKinsey Problem Solving Game, you identify and/or potential “choke points” on the map – preferably close to the center plant, and combine multiple terrain and defenders at that choke point. Blocking terrain, such as mountains, can be used to channel the invaders into the choke points, while bushes and rocks slow them down. Hard-hitting defenders are placed close to the choke point, while the “softer” defenders with longer range form the outer circle.

Here’s a quick illustration of the “kill-zone” tactic:

Contingency planning:

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 scenario.

5. Alternative Scenarios – Disease & Disaster

Early reports also indicate 2 alternative scenarios 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 scenario 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 scenario, 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 scenario, you will solve this scenario only once, unlike the Plant Defense and the next Disease Management scenarios with multiple maps.

5.2. Alternative 2: Disease Management

In the Disease Management scenario 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 scenario 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.

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 scenario 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: Learn to compromise, there is no 100% right answer

The game is designed so that in many cases, there will be shortcomings in even the best solutions – as our test-takers reported. So pay attention to the time limit, and once you’re confident with a “good enough” solution, submit it and proceed to the next scenario. Remember, the time limit is shared by all the scenarios, so spending too much time on one will eat up the necessary time for the next.

Tip 4: Always strive for a better solution

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.

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 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. How to Practice for McKinsey Problem-Solving Game

7.1. Hypothesis-driven problem-solving approach

See this article: Issue Tree, MECE

You may have noticed a lot of the solutions for the scenario 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.


Issue Tree
The Complete Guide

How to Ace Every Consulting Case Interview

I want to know more!

7.2. Mental math and fast reading skills

See this article: Consulting Math, Fast Reading

The McKinsey Problem-Solving Game – especially the 3 ecosystem-related scenarios – 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.

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