<p>I see two ways to interpret the question.</p>
<ol>
<li><p><strong>Combinatorial game theory</strong> often studies (perfect information sequential) games with very large strategy spaces (like chess) often hoping to &quot;solve&quot; them by describing the optimal strategy in some algebraic way. Studying how to partition your first example into passive and aggressive decks, provided this quality determines who wins the game, falls squarely into CGT area. However, CGT is virtually unrelated to standard &quot;economic&quot; GT. Your card game should also be of perfect information like chess, not about uncertainty about your opponent's deck build.</p>
</li>
<li><p>A second way I see to address your question is to look for <strong>equilibrium refinements</strong>. Some games, for example, persuasion and cheap talk games have a huge number of equilibria, but most of them are crazy like your &quot;.. and blink&quot; example. Game theorists have been refining the sets of equilibria to exclude the &quot;crazy&quot; since forever. The trick is to define what the reasonable condition is in every case.</p>
</li>
</ol>

CC BY-SA 4.0
2023-03-04T21:57:46.917Z
2023-03-04T21:57:46.917Z
kitsune
<p>It is clear that players of a game can almost always create trivial variations on strategies without breaking game theoretic conclusions.  For example, a player playing Rock Paper Scissors can play any of...</p>
<ul>
<li>Throw rock and blink</li>
<li>Throw rock and do not blink</li>
<li>Throw paper and blink</li>
<li>Throw paper and do not blink</li>
<li>Throw scissors and blink</li>
<li>Throw scissors and do not blink</li>
</ul>
<p>Game theory would be pretty useless in the real world if solutions to the &quot;new&quot; game this creates could not be analyzed by partitioning this strategy space into the superstrategies...</p>
<ul>
<li>Rock strategies</li>
<li>Paper strategies</li>
<li>Scissors strategies</li>
</ul>
<p>...and applying the tools used to solve the ordinary version of Rock Paper Scissors to these superstrategies (at least up to some imperceptible negative utility of blinking).  Can we, in general, less trivial contexts, port the tools of game theory ordinarily applied to strategies (dominant and dominated strategies, iterative deletion, best responses, Nash Equilibria and other solution concepts, backward induction, etc.) over to these superstrategy partitions of a strategy space?</p>
<p><strong>Example #1:</strong>  In a trading card game, a player builds a custom deck of cards to battle another player with.  Suppose there are <span class="math-container">$5000$</span> cards available, and each deck must contain <span class="math-container">$50$</span> unique cards.  Then the player has <span class="math-container">$5000 \choose 50$</span> possible pure strategies for building a deck or mixing over.  Further suppose we come up with an algorithm which categorizes each strategy as either aggressive (meaning the probability of the resulting deck [or expected deck if mixing] winning a battle is higher in shorter games; it is suited to the role of expending resources ambitiously to seek a fast victory), or passive (meaning the probability of the resulting deck or expected deck winning a battle is higher in longer games; it is suited to the role of waiting for the opponent to expend resources before seeking to win).  We <em>could</em> apply any of the usual tools of game theory to strategies, but the strategy space is huge.  The approach under consideration in this question is to apply the same tools to the superstrategies...</p>
<ul>
<li>Build an aggressive deck</li>
<li>Build a passive deck</li>
</ul>
<p>For example, a superstrategy profile might be considered a Nash Equilibrium iff both players with common knowledge of whether the other has chosen an aggressive deck or a passive deck still do not benefit by switching their own deck from aggressive to passive, or vice-versa.  They are allowed to benefit by switching to a different strategy within the same superstrategy (i.e., switch from one aggressive deck to a different aggressive deck).  Depending on how the finer details are defined, one strategy switch per player may guarantee that no one is better off switching a second time, but if a player is allowed to see that their opponent switched, for example, and that new information incentivizes them to switch again, then no matter how many times players switch while converging to or cycling between strategies, they should not be incentivized to deviate from their superstrategy.</p>
<p><strong>Example #2:</strong>  Consider a three-player game of Monopoly.  I'm not sure if real Monopoly is guaranteed to halt, so assume some arbitrary modifications to the rules to keep things finite, if it matters.  Even if finite, the strategy space is huge, but perhaps we think an important factor for predicting outcomes is knowing which players are included in property trades.  In real Monopoly, players sometimes collude against the player(s) perceived to be the most skilled at the game by refusing to trade with them.  Suppose we design our algorithm to categorize each of Player A's strategies as one of the superstrategies...</p>
<ul>
<li>Attempt to collude with Player B against Player C</li>
<li>Attempt to collude with Player C against Player B</li>
<li>Do not attempt to collude against either opponent</li>
</ul>
<p>Player B and Player C would each have three analogous superstrategies.  Maybe here we want to evaluate whether Player A colluding against Player C is dominated by colluding against Player B.  I can think of at least two senses in which this might be defined...</p>
<ul>
<li>Regardless of which strategies Player B and Player C choose, Player A is always better off playing at least one strategy within the collusion against Player B superstrategy than any strategy within the collusion against Player C superstrategy, though knowing the other players' strategies may influence <em>which</em> strategy within the collusion against Player B superstrategy Player A prefers; the invariant is that no strategy within the collusion against Player C superstrategy is ever better</li>
</ul>
<p>OR</p>
<ul>
<li>Regardless of which superstrategies Player B and Player C choose, Player A is always better off in expectation playing at least one strategy within the collusion against Player B superstrategy than any strategy within the collusion against Player C superstrategy, though learning the other players' exact strategies may incentivize Player A to switch to a different superstrategy</li>
</ul>
<p>Do game theoretic tools extend in this way, possibly with slight modification if I've described them incorrectly?  If so, then can qualitative game theoretic conclusions about a game change depending on how finely or coarsely the strategy space is partitioned into superstrategies?  If not in general, then how far can this idea be pushed beyond the completely trivial examples before it stops producing correct results?</p>

CC BY-SA 4.0
2023-01-29T15:15:46.177Z
"*game theory* **would be** *pretty useless in the real world*" :(
Giskard
CC BY-SA 4.0
2023-02-01T18:42:58.913Z
@Giskard I edited the post to try to flow better.  Hopefully it is a bit more clear.
user10478
CC BY-SA 4.0
2023-02-01T18:46:57.113Z
Hi! Sadly, I find it less clear now :(
Clearly if for all $s$ in 'aggressive strategies' and for all $t$ in 'passive strategies' $s \succ t$, then any aggressive strategy will dominate any passive strategy... so what is the question here?
Giskard
CC BY-SA 4.0
2023-02-01T18:47:58.987Z
Similarly: with iterated elimination of strictly dominated strategies order does not matter, thus if many strategies (a group) are dominated, they can be eliminated all at once.
Giskard
CC BY-SA 4.0
2023-02-01T18:49:58.863Z
My most important question is still, what exactly are you trying to do? If you are trying to do a precise game theoretic thing, can you please give rigorous (math) definitions? If you are trying to do an 'applied' game theory modelling thing, can you explain what? If you are doing some random social science academic mumbo jumbo... just have a nice day!
Giskard
CC BY-SA 4.0
2023-02-02T21:37:07.483Z
@Giskard Okay, I've done a more radical rewrite and I believe the explanation should be clearer now!
user10478
CC BY-SA 4.0
2023-02-03T07:53:12.183Z
Could you please you answer my question of what your goal is though? Or do you not have one, and this is random intellectual curiosity?
Giskard
CC BY-SA 4.0
2023-02-03T17:57:32.867Z
@Giskard My actual goal is much in the vicinity of the two examples given in the question.  Some games have "metagame" concepts, such as the general play style of a deck in a trading card game, or the impact collusion has on each player's probability of winning a three or more player game, which are more easily understood at a higher level of abstraction than individual actions and payoffs.  My goal is to figure out when and how I can learn something about such a metagame concept using what I know about game theory, and how conclusions change along the dimension between a
user10478
CC BY-SA 4.0
2023-02-03T17:57:37.137Z
particular abstract representation of a game's strategy space and the maximally fleshed out representation of its strategy space ordinarily studied in an introductory game theory presentation.
user10478
<game-theory><nash-equilibrium><dominant-strategy>
Can game theoretic concepts be applied to any groups of strategies collectively partitioning the strategy space?
user10478
# Can game theoretic concepts be applied to any groups of strategies collectively partitioning the strategy space?

(Asked by: username_0 on 2023-01-29)

It is clear that players of a game can almost always create trivial variations on strategies without breaking game theoretic conclusions. For example, a player playing Rock Paper Scissors can play any of...

* Throw rock and blink
* Throw rock and do not blink
* Throw paper and blink
* Throw paper and do not blink
* Throw scissors and blink
* Throw scissors and do not blink

Game theory would be pretty useless in the real world if solutions to the "new" game this creates could not be analyzed by partitioning this strategy space into the superstrategies...

* Rock strategies
* Paper strategies
* Scissors strategies

...and applying the tools used to solve the ordinary version of Rock Paper Scissors to these superstrategies (at least up to some imperceptible negative utility of blinking). Can we, in general, less trivial contexts, port the tools of game theory ordinarily applied to strategies (dominant and dominated strategies, iterative deletion, best responses, Nash Equilibria and other solution concepts, backward induction, etc.) over to these superstrategy partitions of a strategy space?

**Example #1:** In a trading card game, a player builds a custom deck of cards to battle another player with. Suppose there are $5000$ cards available, and each deck must contain $50$ unique cards. Then the player has $5000 \choose 50$ possible pure strategies for building a deck or mixing over. Further suppose we come up with an algorithm which categorizes each strategy as either aggressive (meaning the probability of the resulting deck [or expected deck if mixing] winning a battle is higher in shorter games; it is suited to the role of expending resources ambitiously to seek a fast victory), or passive (meaning the probability of the resulting deck or expected deck winning a battle is higher in longer games; it is suited to the role of waiting for the opponent to expend resources before seeking to win). We *could* apply any of the usual tools of game theory to strategies, but the strategy space is huge. The approach under consideration in this question is to apply the same tools to the superstrategies...

* Build an aggressive deck
* Build a passive deck

For example, a superstrategy profile might be considered a Nash Equilibrium iff both players with common knowledge of whether the other has chosen an aggressive deck or a passive deck still do not benefit by switching their own deck from aggressive to passive, or vice-versa. They are allowed to benefit by switching to a different strategy within the same superstrategy (i.e., switch from one aggressive deck to a different aggressive deck). Depending on how the finer details are defined, one strategy switch per player may guarantee that no one is better off switching a second time, but if a player is allowed to see that their opponent switched, for example, and that new information incentivizes them to switch again, then no matter how many times players switch while converging to or cycling between strategies, they should not be incentivized to deviate from their superstrategy.

**Example #2:** Consider a three-player game of Monopoly. I'm not sure if real Monopoly is guaranteed to halt, so assume some arbitrary modifications to the rules to keep things finite, if it matters. Even if finite, the strategy space is huge, but perhaps we think an important factor for predicting outcomes is knowing which players are included in property trades. In real Monopoly, players sometimes collude against the player(s) perceived to be the most skilled at the game by refusing to trade with them. Suppose we design our algorithm to categorize each of Player A's strategies as one of the superstrategies...

* Attempt to collude with Player B against Player C
* Attempt to collude with Player C against Player B
* Do not attempt to collude against either opponent

Player B and Player C would each have three analogous superstrategies. Maybe here we want to evaluate whether Player A colluding against Player C is dominated by colluding against Player B. I can think of at least two senses in which this might be defined...

* Regardless of which strategies Player B and Player C choose, Player A is always better off playing at least one strategy within the collusion against Player B superstrategy than any strategy within the collusion against Player C superstrategy, though knowing the other players' strategies may influence *which* strategy within the collusion against Player B superstrategy Player A prefers; the invariant is that no strategy within the collusion against Player C superstrategy is ever better

OR

* Regardless of which superstrategies Player B and Player C choose, Player A is always better off in expectation playing at least one strategy within the collusion against Player B superstrategy than any strategy within the collusion against Player C superstrategy, though learning the other players' exact strategies may incentivize Player A to switch to a different superstrategy

Do game theoretic tools extend in this way, possibly with slight modification if I've described them incorrectly? If so, then can qualitative game theoretic conclusions about a game change depending on how finely or coarsely the strategy space is partitioned into superstrategies? If not in general, then how far can this idea be pushed beyond the completely trivial examples before it stops producing correct results?

**Tags:** game-theory, nash-equilibrium, dominant-strategy

> **Comments:**
> **username_1**: "*game theory* **would be** *pretty useless in the real world*" :(  
> **username_0**: @username_1 I edited the post to try to flow better.  Hopefully it is a bit more clear.  
> **username_1**: Hi! Sadly, I find it less clear now :( Clearly if for all $s$ in 'aggressive strategies' and for all $t$ in 'passive strategies' $s \succ t$, then any aggressive strategy will dominate any passive strategy... so what is the question here?  
> **username_1**: Similarly: with iterated elimination of strictly dominated strategies order does not matter, thus if many strategies (a group) are dominated, they can be eliminated all at once.  
> **username_1**: My most important question is still, what exactly are you trying to do? If you are trying to do a precise game theoretic thing, can you please give rigorous (math) definitions? If you are trying to do an 'applied' game theory modelling thing, can you explain what? If you are doing some random social science academic mumbo jumbo... just have a nice day!  
> **username_0**: @username_1 Okay, I've done a more radical rewrite and I believe the explanation should be clearer now!  
> **username_1**: Could you please you answer my question of what your goal is though? Or do you not have one, and this is random intellectual curiosity?  
> **username_0**: @username_1 My actual goal is much in the vicinity of the two examples given in the question.  Some games have "metagame" concepts, such as the general play style of a deck in a trading card game, or the impact collusion has on each player's probability of winning a three or more player game, which are more easily understood at a higher level of abstraction than individual actions and payoffs.  My goal is to figure out when and how I can learn something about such a metagame concept using what I know about game theory, and how conclusions change along the dimension between a  
> **username_0**: particular abstract representation of a game's strategy space and the maximally fleshed out representation of its strategy space ordinarily studied in an introductory game theory presentation.  

---

## 1 answers

### Answer by username_2 on 2023-03-04. Score: 1

I see two ways to interpret the question.

1. **Combinatorial game theory** often studies (perfect information sequential) games with very large strategy spaces (like chess) often hoping to "solve" them by describing the optimal strategy in some algebraic way. Studying how to partition your first example into passive and aggressive decks, provided this quality determines who wins the game, falls squarely into CGT area. However, CGT is virtually unrelated to standard "economic" GT. Your card game should also be of perfect information like chess, not about uncertainty about your opponent's deck build.
2. A second way I see to address your question is to look for **equilibrium refinements**. Some games, for example, persuasion and cheap talk games have a huge number of equilibria, but most of them are crazy like your ".. and blink" example. Game theorists have been refining the sets of equilibria to exclude the "crazy" since forever. The trick is to define what the reasonable condition is in every case.


---

