GeekNights Food Chain Magnate Design Analysis

Some very interesting discussion of the board game Food Chain Magnate by the GeekNight guys. I’ve seen these guys floating around doing GDC talks and a couple other places, but this is some particularly insightful commentary. Check it out!

Some game design talk about “big stuff never used”, “attractors”, heuristic tress, the learning process for games, and abstractly the information horizon!

Alakaram!

In case you didn’t know, I’m a HUGE fan of Auro: A Monster-Bumping Adventure (Steam link). When you get right down to it, this game had a HUGE influence on my game design thinking, especially with respect to the kinds of pared-down, endlessly interesting little decision-making machines that I want to keep making! Auro, and Keith Burgun’s writing about a single-player ladder ladder/procedural content system (among lots of other interesting game-design insights!) have become the basis for BrainGoodGames!

Long story short, Auro is awesome, Dinofarm is awesome, Keith Burgun is awesome, and I’m super excited to follow and play their spiritual successor to Auro Alakaram! Of particular interest is this idea of “Chaff” spells, which may or may not be balanced, but are included for the sake of variety for players who favor that (or sometimes do) over serious, meticulously balanced play. I think there’s probably a ton of room for spells like this and I can’t wait to try em out!

Awesome!

Game Design Blogs!

There’s been an awesome resurgence on the Dinofarm discord of people blogging about game design! Tons to think about and great to have a community of people to bounce ideas off of. 

You can check out a list that Redless made on the Dinofarm forums here!

http://www.dinofarmgames.com/forum/index.php?threads/game-design-projects.2650

Commandment #7: Challenge

This post is part of a series of articles detailing the BrainGoodGames Design Commandments. You can see the full list here.

In truth, this commandment is actually very closely related to the commandment about Learning. This is because if there are no gameplay paths that the player can come up with that give a reasonable chance of victory, or if the player can choose an arbitrary path (i.e if the game is too easy or too hard), then the player either cannot or does not need to learn anything! If we accept the premise that learning is a huge aspect of the fun of strategy games, then providing the correct challenge level is absolutely critical in creating an enjoyable experience.

Games have tried many methods of tailoring the challenge level correctly over the years, some overtly, others more subtly. One classic example is the concept of “grinding” in early RPG systems like Final Fantasy. The ability to grind (gaining power through some repetitive action) ensures that players of any skill level can eventually surpass whatever challenge the game throws at them. In theory this allows players to correctly tailor the challenge level to themselves. In practice it often results in players grinding until the game presents no strategic challenge (as players will often use the simplest solution available, even if it is less fun for them!). Not only that, but grindingly inherently involves engaging in a low-value activity in exchange for power, which means part of your game system is inherently boring to engage with (setting aside the quirk of human nature which assign some base-line satisfaction with gaining quantifiable power, as evidenced by clicker games).

Another common method of scaling difficulty is to allow players to simply select the difficulty from a menu at the start of the game. This is problematic as the player has no way of knowing which difficulty level will be appropriate for them in your game system until they have engaged with it. A more modern “fix” is to allow players to re-select their difficulty at any time from a menu. The problem with this approach is that it again gives players a trivial way to bypass the challenge and learning of your game, and therefore a lot of the fun! Additionally, I think that part of the job of a game designer is to do upfront work to craft a game experience the player will find enjoyable, and to some extent that includes selecting the difficulty. Players shouldn’t have to do the designer’s work for them (although I will admit that at times this is not feasible, in which case practical concessions need to be made).

Am I more of a “Ultra-Violence” guy or a “Nightmare!” guy…

BrainGoodGames have taken another approach, as outlined in the article on Learning. They use a single-player ladder system to develop a sense of player’s skill, and then adapt dynamically to continually modify the challenge to be suitable for them! In my estimation this solves a lot of the fun-circumvention problems of other systems, and removes some design burden from players. Win-win! A further augmentation present in Militia, and likely soon to be included in other BrainGoodGames is the inclusion of a “Placement Match” system to allow players who feel the difficulty is incorrectly calibrated to quickly set it to a (ideally) closer challenge level. This also allows experienced strategy game players to skip ranks that are too easy for them, and players’ to opt in to re-calibration after a large balance patch!

Commandment #6: Learning

This post is part of a series of articles detailing the BrainGoodGames Design Commandments. You can see the full list here.

Raph Koster explains in his novel “A Theory of Fun” that perhaps the most important source of enjoyment in a game comes from learning. I absolutely agree with this claim, and framing things in this way can allow us to make many inferences about the ideal way to design our games.

As explained in my recent article, carefully crafted ambiguity allows a designer to continually present the player with novel and yet at least somewhat comparable situations to those they have encountered before. In this context, players are able to use their existing knowledge of a game’s system, without being able to rely on rote memorization of solutions to succeed. As they receive feedback (most importantly through whether they won or lost), they can develop new insights into the system (i.e learn).

However, this is not quite enough, because there is another way that learning can be prevented that is quite common. If the game situation is too easy to figure out for the player, they can simply pick an arbitrary strategy and win, without weighing/considering several options (strategic thinking). If the game situation is too hard/beyond their capability to figure out, then all of the strategic paths they come up with will be equally ineffective (resulting in a loss). Either way, learning is stifled. 

Fortunately, multiplayer match-based games have already come up with an ingenious solution to this problem: a matchmaking/ladder ranking system! In such a system, players are (theoretically) matched against opponents that provide suitable difficulty for them. As Keith Burgun points out, such a system can also be applied in a single-player context in much the same way. As players win, the game gets harder, and as they lose, the game gets easier. At some point, they will be placed into matches of an appropriate challenge level (which allows for optimal learning!). This process can even be sped up by doing a “placement match” to estimate what rank/difficulty they should start at. 

This does beg the question of how to scale the system mathematically to increase the challenge in a way that does not feel arbitrary (not to mention designing a system that rewards learning in the first place). This is one of the primary challenges of designing a single-player strategy game in my opinion, and needs to be considered early on in the process. It is absolutely possible to come up with satisfying answers, and provide reasonable scaling up to a very high level of skill.

The single-player ladder system in Minos Strategos.

As one final point, I want to mention that a system such as this has the added benefit of reflecting a player’s growth and learning in a tangible way! By ranking up a player is able to see with some degree of certainty that they have in fact improved at understanding the system strategically, which is an awesome side effect.

Commandment #5: Ambiguity

This post is part of a series of articles detailing the BrainGoodGames Design Commandments. You can see the full list here.

A strategy game is a delicate thing. Humans are excellent at using heuristics to reduce complex problems to simple rules of thumb, and the formation of those heuristics (learning) is a huge part of the enjoyment. However, if a game features too much rules complexity, the player will spend their time trying to internalize the rules rather than engaging in higher level strategic thinking (more on this in Commandment #1 here). On the other hand, the less inherent (rules) complexity a game has, the easier it is for human minds to “solve” it, at which point it becomes more like a puzzle than a game. A classic example of a game that is highly solvable is tic-tac-toe.

Games do have a secret weapon to employ to help resist solvability, while maintaining some degree of elegance: Ambiguity. Interestingly, one of the most prevalent and oldest forms of ambiguity in games is simply another player! You are unable to know definitively what other players will do in a game (although some games are designed to test your ability to guess), so even in the case of a very deterministic system like Chess, your opponent contributes to a very high degree of unpredictability/unsolvability (such that we have played Chess for many many years without “solving” it).

However, in a single-player strategy game, we do not have the luxury of all this free ambiguity; designers must thoughtfully add it themselves. The most natural solution may seem to be to come up with an intelligent AI opponent to play against. This has a few drawbacks. Firstly, in a practical sense, playing against an AI opponent in a strategy game frequently has players casting about for ways to exploit the patterns in the behavior of the AI, rather than trying to form a deep understanding of the game system itself. Secondly, if we think of an AI as a set of rules, adding even the simplest AI to our game has a huge ballooning effect on the rules complexity of our game. Players may not need to know about the AI behavior, but they can learn about it, and doing so pushes against learning how the core systems of the game interact strategically.

Fortunately, there are many more techniques available to a game designer to add ambiguity to their games. Dice, cards and coins provide convenient metaphors so that the player can understand the possible outcomes and their likelihood without having a big reference table. Procedural generation is another fantastic tool for providing new non-rules information to the player while they have time to strategically react. The enemy movements in Militia are yet another example of providing new information for future turns. Each enemy chooses from ~4-8 possible moves, within simple rules, which has the effect of mixing up the board for next turn, but in a way that can be understood and planned for.

This board is a product of the board that came before it plus some simple enemy movement rules.

There are absolutely ways in which introducing too much ambiguity, or ambiguity that the player cannot react to or plan around can reduce the learning potential of your system or make it unsatisfying to play. I’ll be going into more detail about my take on the different forms ambiguity can take in later articles, but for now you can check out Keith Burgun’s excellent article on the subject.