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The wagon must travel a path that is given by a mathematical function.

A mathematical game is a game whose rules, strategies, and outcomes are defined by clear mathematical parameters.[1][verification needed][clarification needed] Often, such games have simple rules and match procedures, such as tic-tac-toe and dots and boxes. Generally, mathematical games need not be conceptually intricate to involve deeper computational underpinnings. For example, even though the rules of Mancala are relatively basic, the game can be rigorously analyzed through the lens of combinatorial game theory.[citation needed]

Mathematical games differ sharply from mathematical puzzles in that mathematical puzzles require specific mathematical expertise to complete, whereas mathematical games do not require a deep knowledge of mathematics to play. Often, the arithmetic core of mathematical games is not readily apparent to players untrained to note the statistical or mathematical aspects.[citation needed]

Some mathematical games are of deep interest in the field of recreational mathematics.[2][verification needed]

When studying a game's core mathematics, arithmetic theory is generally of higher utility than actively playing or observing the game itself. To analyze a game numerically, it is particularly useful to study the rules of the game insofar as they can yield equations or relevant formulas. This is frequently done to determine winning strategies or to distinguish if the game has a solution.[citation needed]

Additionally, mathematical games can aid children in grasping fundamental concepts such as addition, subtraction, multiplication,[3] and division, enhancing their arithmetic skills in an engaging manner.

List of games

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Sometimes it is not immediately obvious that a particular game involves chance. Often a card game is described as "pure strategy" and such, but a game with any sort of random shuffling or face-down dealing of cards should not be considered to be "no chance". Several abstract strategy games are listed below:

Lattice board

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Non-lattice boards and other games

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Chance involved or imperfect information

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See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Mathematical game

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A mathematical game is a form of , puzzle, or recreational activity whose rules, strategies, and outcomes are defined by clear mathematical parameters, often integrating concepts such as arithmetic, , logic, or probability. These can range from single-player puzzles to multiplayer strategic contests, with or without elements of chance. Mathematical games have ancient origins, dating back to puzzles in Rhind Papyrus around 1850 BC and Chinese magic squares by 2200 BC. Over time, works like Fibonacci's (1202) and Bachet de Méziriac's Problèmes plaisans et délectables (1612) advanced , while Euler's 18th-century analyses laid foundations for . They serve educational purposes, fostering skills like problem-solving and in classrooms, as well as recreational challenges. Examples include geometrical puzzles like tangrams, board games like (originating in around 700 AD), and combinatorial games like . A key area of study is , which analyzes impartial, two-player games of without chance, such as —whose complete theory was developed in 1901 by Charles Bouton (with the Sprague-Grundy theorem generalizing it in the 1930s). These deterministic games can be decomposed into subgames, with applications in . Overall, mathematical games bridge play and rigorous .

Definition and Scope

Core Definition

A mathematical game is a structured pedagogical or recreational activity that integrates mathematical concepts, such as arithmetic, , or logic, into gameplay, typically involving one or more players who make strategic decisions under defined rules to achieve an outcome, often with minimal reliance on chance. These games can range from simple puzzles and solo challenges to multi-player contests, emphasizing problem-solving, , and . A significant subset falls under (CGT), which focuses on impartial, two-player games of without chance elements, where players alternate turns until a terminal position is reached. In CGT, games are finite and acyclic, ensuring termination. Combinatorial games are defined with a set of positions PP, terminal positions T⊆PT \subseteq P (losses for the player to move under normal play), and move options O:P∖T→P(P)∖{∅}\mathcal{O}: P \setminus T \to \mathcal{P}(P) \setminus \{\emptyset\}. However, the broader category of mathematical games extends beyond these formal constraints to include educational exercises and recreational puzzles.

Distinctions from Other Games

Mathematical games differ from general recreational or entertainment games primarily in their explicit incorporation of mathematical principles, such as numerical operations, spatial reasoning, or combinatorial strategies, rather than purely social, physical, or chance-based elements. While many recreational games like board games or video games may include math incidentally, mathematical games center on mathematical engagement to enhance learning or challenge . Unlike games dominated by chance (e.g., dice-rolling without strategy) or imperfect information (e.g., card games with hidden hands), mathematical games prioritize strategic based on observable rules and , though some variants include probabilistic or hidden elements as classified in later sections. They assume rational play where possible, but outcomes can vary with player skill in broader contexts, contrasting with purely deterministic analyses in CGT subsets like . Mathematical games exclude purely non-mathematical pursuits, such as athletic sports or narrative-driven adventures, focusing instead on discrete challenges amenable to or . This includes avoiding continuous or infinite move spaces in standard forms, though theoretical extensions exist. Their analyzable nature stems from defined rules enabling evaluation of strategies, bridging fun and formal .

Historical Development

Ancient and Early Examples

One of the earliest known mathematical games is , originating in around 3500 BCE during the predynastic period. This featured a linear board of 30 squares, where two players advanced five pieces each from start to finish by throwing casting sticks or to determine moves, with specific squares imposing constraints such as safe havens or penalties that required pieces to retreat. The game's structure encouraged strategic blocking and overtaking, embodying basic combinatorial decision-making in move selection. In , precursors to emerged around 3000 BCE, exemplified by of , a played on a board with 20 squares divided into paths for opposing pieces. Players rolled four-sided dice made from pyramids to move seven pieces toward a finish line, incorporating rules for capturing opponents and safe rosettes that provided immunity, highlighting combinatorial elements like probability in dice outcomes and path optimization. This game, popular among Sumerian elites and commoners alike, influenced later through its blend of chance and strategy. Ancient Indian games like , with evidence of play dating back to the CE as indicated by historical records, utilized a board where players raced pieces around a cross-shaped path using cowrie shells for . The game's layout lent itself to graph-theoretic interpretations, with the board representing vertices and edges for possible moves, allowing players to count paths and anticipate blockages in a multi-route race. Such features underscored intuitive spatial reasoning in navigating interconnected routes. European developments in the 18th and 19th centuries included puzzles like the , introduced in 1883 by French mathematician as an involving three pegs and stacked discs of decreasing size. The objective was to relocate the entire stack from one peg to another, adhering to the rule that no larger disc could rest on a smaller one, which demanded systematic planning of recursive sequences without chance elements. This solitary puzzle exemplified impartial combinatorial challenges, solvable through minimal moves calculated via powers of 2. These ancient and early were analyzed intuitively by players long before formal , often relying on in board layouts to predict outcomes or counting possible paths to strategize advances, as evidenced in historical artifacts and rule inscriptions that imply such practical heuristics.

Modern Formalization

The modern formalization of began in the early with efforts to apply rigorous to strategic interactions, shifting from informal play to systematic . In 1921, published foundational work on two-person zero-sum , proposing probabilistic strategies for symmetric cases and laying groundwork for later developments in , though his focus was more on continuous rather than discrete combinatorial structures. This precursor work emphasized the potential for mathematical resolution in games where one player's gain equals the other's loss. Building on Borel's ideas, advanced the field in 1928 with his , which guarantees the existence of optimal mixed strategies in finite two-person zero-sum games, including discrete ones like those in combinatorial settings. The theorem, proved using the Brouwer fixed-point theorem, established that the maximum value equals the minimum maximin value, providing a cornerstone for analyzing strategic equilibrium in perfect-information games. Von Neumann's contribution extended to broader applications in and , influencing the treatment of discrete games as mathematical objects. A pivotal milestone came with the independent discoveries of the Sprague-Grundy theorem for impartial games. In 1935, Roland Sprague demonstrated that any impartial combinatorial game under normal play is equivalent to a heap, assigning a Grundy number to each position based on the mex (minimum excludant) of its options' values. Patrick Grundy independently proved the same result in 1939, showing that the overall game's value is the nim-sum of component games' values, enabling recursive solution methods for complex impartial positions. These works formalized the analysis of impartial games, where both players have identical moves, bridging earlier studies to general theory. Post-World War II developments in the 1950s extended these ideas to partisan games, where players have differing move options. David Gale contributed to foundational aspects of perfect-information games through papers on finite two-person strategies, influencing the shift toward partizan analysis. Contemporaries like in 1953 introduced concepts for comparing partizan positions via sums and differences, while Olof Hanner in 1959 published work on sums of positional games inspired by Go, advancing partizan analysis and paving the way for richer combinatorial structures. These extensions broadened the scope beyond impartiality, incorporating asymmetry in a mathematical framework. The 1980s marked the formation of a dedicated International Combinatorial Game Theory community, catalyzed by the publication of Winning Ways for Your Mathematical Plays in 1982 by , John Conway, and Richard Guy, which synthesized and popularized the theory for both impartial and partizan games. This collaborative effort, building on prior milestones, fostered ongoing research, workshops, and a unified , transforming into a vibrant subfield of .

Fundamental Concepts in Combinatorial Game Theory

Impartial vs. Partisan Games

In combinatorial game theory, a fundamental distinction exists between impartial and partisan games, based on the symmetry of move options available to the players from any position. Impartial games are defined such that both players have identical sets of legal moves from every position, ensuring complete symmetry in the rules regardless of who is to move. This property simplifies analysis, as demonstrated by the Sprague-Grundy theorem, which assigns a non-negative integer value known as a nimber (or Grundy number) to each position; the theorem states that the disjunctive sum of impartial games is equivalent to a Nim game whose heap sizes correspond to the XOR of the individual nimbers. Independently formulated by Roland Sprague in 1936 and Patrick Grundy in 1939, this theorem reduces complex impartial games to simpler Nim equivalents, facilitating winning strategy determination under normal play conventions. Partisan games, by contrast, lack this symmetry: the set of available moves depends on which player is acting, often formalized as distinct "left" options (for one player) and "right" options (for the other) from each position. Such games require advanced tools for valuation, including John Conway's surreal numbers, where positions are represented as ordered pairs of surreal forms and analyzed via disjunctive sums that correspond to surreal addition rather than XOR. This framework, developed in Conway's work, accommodates the asymmetry by embedding game values in a broader that captures advantages for either player. The implications of this dichotomy are evident in strategic analysis: impartial games leverage nimber arithmetic for efficient computation of outcomes in sums, whereas partisan games demand the full surreal number machinery to evaluate comparative advantages, often yielding infinitesimal or infinite values that reflect subtle biases. For instance, exemplifies an , with players alternately removing objects from shared heaps under identical rules, allowing direct application of Sprague-Grundy values. In opposition, Domineering illustrates a partisan game, where one player tiles a grid with vertical and the other with horizontal ones, creating inherently asymmetric move opportunities that resist nimber reduction. This classification, central to modern as articulated in Berlekamp, Conway, and Guy's seminal work, underscores how player symmetry dictates the appropriate analytical toolkit.

Positions and Moves

In , positions represent the states of a game and are formally defined as vertices (nodes) in a known as the game graph. Each position can be classified as (starting state), intermediate (during play), or terminal (end states where no further moves are possible). Terminal positions are those with no outgoing edges, and under normal play convention—where the player unable to move loses—they are P-positions, meaning the previous player wins with optimal play. In contrast, non-terminal positions are evaluated recursively to determine if they are P-positions (all moves lead to N-positions, where the next player wins) or N-positions (at least one move leads to a P-position). Moves are represented as directed edges connecting positions in the game graph, indicating legal transitions from one state to another. For games of finite length, the graph is acyclic (a directed acyclic graph, or DAG), ensuring no cycles that could lead to infinite play and guaranteeing termination. The rules of the game specify the possible moves from each position, with players alternating turns to select an edge to a successor position. The value of a position is determined recursively based on the values of the positions reachable by moves, allowing players to assess winning strategies. Specifically, a position is winning for the current player if there exists a move to a losing position for the opponent; otherwise, it is losing. In the general framework for partisan games, where players may have different move options, a position GG is denoted as G={GL∣GR}G = \{ G^L \mid G^R \}, where GLG^L is the set of positions Left can move to and GRG^R is the set for Right. This notation supports the recursive definition, with the game's outcome derived from comparing options for both players.

Classification of Mathematical Games

Deterministic Perfect-Information Games

Deterministic perfect-information games constitute a fundamental class within mathematical games, characterized by the absence of any random elements and complete visibility of the game state to both players at every turn. In these games, outcomes depend solely on the players' choices, with no hidden or probabilistic transitions influencing the progression. This setup ensures that the entire of moves is known, allowing each player to base decisions on the full current position. Key properties of such games include their finiteness, meaning they reach a terminal position after a bounded number of moves, and their classification as either impartial—where both players have identical available moves from any position—or partisan, where move options differ by player. These games are analyzable through , a method that evaluates positions starting from terminal states and propagating winning or losing classifications upward through the game tree. Zermelo's theorem establishes that in finite two-player deterministic perfect-information games without draws, one player possesses a winning strategy from the initial position. A primary distinction in these games arises between normal play, where the player making the last move wins, and misère play, where the last move results in a loss. Standard combinatorial analysis prioritizes normal play, as it aligns with algebraic structures like the Sprague-Grundy theorem for impartial games, though misère variants introduce complexities that deviate from these frameworks, often requiring separate evaluation for short games. Positions and moves serve as the foundational units, with each position representing a state from which legal moves lead to subsequent positions. Analytical tools for these games rely on constructing game trees, which enumerate all possible move sequences, enabling exhaustive search to determine optimal strategies in small instances. For larger games, escalates exponentially due to the of the tree, rendering full enumeration infeasible beyond modest sizes and necessitating or partial evaluation methods.

Games with Chance

Games with chance introduce probabilistic elements into mathematical games, where outcomes or available moves depend on random events such as rolls, card draws, or randomized state transitions. These elements distinguish them from purely strategic games by incorporating that affects player decisions and game analysis. A classic example is variants of where initial heap sizes are determined randomly, altering the starting position according to a and requiring players to adapt strategies to potential configurations. The primary analysis challenges in these games stem from the need to compute expected values over possible outcomes and to model probability distributions across game positions. Unlike deterministic games, where exact winning strategies can be derived combinatorially, games with chance demand probabilistic tools to evaluate the likelihood of success from any state, often involving integration of and reward in selection. For instance, players must weigh the expected of a move against the variance introduced by chance, using methods like dynamic programming to approximate optimal policies. Central to this framework are stochastic games, formalized as multiplayer extensions of Markov decision processes, where states evolve probabilistically based on joint player actions and chance. The value of a position in such games is determined recursively via Bellman equations, capturing the discounted expected payoff: V(s)=max⁡a[∑pp⋅r+γV(s′)]V(s) = \max_a \left[ \sum_p p \cdot r + \gamma V(s') \right] This equation highlights how immediate rewards rr and future values V(s′)V(s') are weighted by transition probabilities pp and discount factor γ\gamma, though in competitive settings, the maximization reflects the equilibrium value of underlying matrix games. Optimal strategies in with chance thus blend combinatorial insight with , as players select actions that maximize expected long-term advantage while accounting for uncontrollable random variations. This integration often leads to mixed strategies, where pure deterministic moves are augmented by hedging against unfavorable chance outcomes to minimize potential losses. In contrast to deterministic perfect-information , this probabilistic layer shifts focus from guaranteed wins to robust performance under uncertainty.

Imperfect-Information Games

Imperfect-information games in mathematical game theory are characterized by partial , where players do not possess full of the current game state, including others' prior actions, private information, or certain environmental parameters. This lack of contrasts with perfect-information scenarios and often manifests through mechanisms such as private signals, hidden attributes, or fog-of-war elements that obscure parts of the board or state from opponents. For instance, a player might know their own hand but not the opponent's, leading to about the overall configuration. To mathematically represent these games, the tree incorporates information sets, which are partitions of the tree's nodes that a player cannot distinguish between due to identical observable histories. Each information set groups decision points where the player has the same partial view of the game, ensuring that strategies specify actions consistently across indistinguishable situations. This structure, formalized in the foundational work on extensive games, allows for the modeling of uncertainty without assuming full recall or observability. Analysis of imperfect-information games typically relies on Bayesian methods to update players' beliefs about unobserved states or actions. Upon receiving new signals or observations, players apply Bayes' rule to revise prior probabilities: the posterior belief Pr⁡(F∣E)=Pr⁡(E∣F)Pr⁡(F)Pr⁡(E)\Pr(F \mid E) = \frac{\Pr(E \mid F) \Pr(F)}{\Pr(E)}, where FF represents a hypothesized state and EE an observed event, enabling rational decision-making under uncertainty. Complementing this, solution concepts such as trembling-hand perfect equilibrium refine standard Nash equilibria by considering small, unintentional deviations ("trembles") in opponents' strategies, ensuring robustness even when players might err slightly with positive probability. Introduced by , this equilibrium requires strategies to remain optimal in the limit as tremble probabilities approach zero, addressing implausible outcomes in games with hidden information. A central challenge in these stems from , which introduces non-determinism into strategy selection and can foster behaviors like bluffing—where a player misrepresents their position to mislead opponents—or signaling, where actions convey credible information about private types to influence beliefs. This asymmetry complicates equilibrium computation, as players must anticipate how others infer and respond to partial revelations, often leading to multiple possible equilibria without clear selection criteria. While imperfect information sometimes overlaps with chance elements in hybrid models, the focus here remains on epistemic uncertainty rather than observable randomness.

Notable Examples and Analysis

Lattice-Based Games

Lattice-based games constitute a significant subclass of combinatorial games, played on structured grids or lattices such as square, triangular, or hexagonal arrangements of points connected by edges. In these games, players typically alternate placing tokens on unoccupied vertices or cells, or traversing edges according to predefined rules, with the underlying lattice providing a geometric framework that introduces spatial constraints and symmetries. The regular structure of lattices allows for the application of graph-theoretic analyses, where the game board is modeled as a graph with vertices representing positions and edges defining legal moves. A prominent example is , played on a 3×3 , where players aim to form a connected line of three marks horizontally, vertically, or diagonally. Analysis reveals that with optimal play, the game ends in a draw due to the of the lattice, which enables the second player to and block all winning paths; this can be demonstrated through exhaustive enumeration of the 255,168 possible games, confirming no forced win for the first player. Another key example is Go, contested on a 19×19 (approximating an infinite lattice for local tactical analysis), where players surround territory to score points under area or territory scoring rules. The game's complexity arises from the lattice's connectivity, allowing emergent properties like of groups, with applied to endgame positions to value local battles as sums of subgames. Mathematical tools for analyzing lattice-based games include techniques to identify winning configurations, such as coloring the lattice to avoid monochromatic paths that constitute a win, drawing from in positional games. Pairing strategies also prove effective for establishing second-player wins, where the defender pairs potential winning lines and responds to threats by occupying the paired position, neutralizing the attacker's advances on finite or infinite grids. Many lattice-based games are partisan, meaning the available moves differ for each player, often due to directional constraints on the lattice; for instance, in , played on a lattice, one player places vertical dominoes while the other places horizontal ones, creating asymmetric options that require for valuation. This directionality exploits the lattice's orthogonal structure, leading to values expressible as differences of impartial games under normal play convention.

Non-Lattice Board and Abstract Games

Non-lattice board and abstract games in involve positions composed of disjoint components, such as multiple heaps or abstract sets, where moves affect only one component at a time, enabling the analysis of the overall game as a sum of independent subgames. These games lack the fixed geometric lattices of spatial board games, focusing instead on combinatorial structures where the independence of components allows for recursive evaluation using tools like the Sprague-Grundy theorem. Unlike lattice-based games that incorporate positional geometry, non-lattice games prioritize the modularity of disjoint parts for determining winning strategies. A foundational example is the game of , where players alternate removing any positive number of objects from a single heap among several disjoint heaps, with the player unable to move losing. Charles Bouton provided the complete mathematical theory in 1901, showing that the second player wins if the bitwise XOR of all heap sizes is zero, and otherwise the first player can force a win by balancing the XOR to zero after their move. The Sprague-Grundy theorem, independently developed by Roland Sprague in 1936 and Patrick Grundy in 1939, formalizes this by assigning to each position a Grundy number (or ), defined as the minimum excludant (mex) of the Grundy numbers of positions reachable in one move: g(P)=mex{g(P′)∣P′ is a move from P}g(P) = \mathrm{mex} \{ g(P') \mid P' \text{ is a move from } P \} where mex is the smallest non-negative integer not in the set. In Nim, the Grundy number of a heap of size nn is simply g(n)=ng(n) = n, since moves lead to heaps of sizes 0 through n−1n-1. For impartial games like these, the disjunctive sum G+HG + H—where a move is made in exactly one summand—has Grundy number g(G+H)=g(G)⊕g(H)g(G + H) = g(G) \oplus g(H), with ⊕\oplus denoting bitwise XOR; a position is losing if and only if its overall Grundy number is zero. Kayles exemplifies non-lattice abstract games played on graphs, specifically a path graph representing a row of bowling pins, where a move consists of selecting one or two adjacent pins to knock down, removing them and isolating adjacent pins. This can be viewed as playing on disjoint chains after moves, with the game decomposing into sums of independent components. The Grundy numbers for Kayles positions are computed recursively but exhibit eventual periodicity, though the exact values for finite chains are intricate and tabulated in analyses like those in Berlekamp, Conway, and Guy's work, revealing non-trivial patterns in winning and losing positions. Subtraction games form another class of abstract heap games, where from a single heap of size nn, a player removes a number of objects in a fixed SS, such as S={1,2}S = \{1, 2\}. The Sprague-Grundy theorem applies directly, yielding Grundy numbers that are eventually periodic for finite SS; for S={1,2}S = \{1, 2\}, the sequence is g(0)=0g(0) = 0, g(1)=1g(1) = 1, g(2)=2g(2) = 2, g(3)=0g(3) = 0, g(4)=1g(4) = 1, and so on, repeating every 3 terms as 0, 1, 2. Multiple heaps in subtraction games combine via disjunctive sum, with the XOR of their Grundy numbers determining the outcome, highlighting the power of this framework for solving multi-component impartial games.

Games Involving Probability or Hidden Information

Games involving probability introduce elements of chance, such as random outcomes from or cards, which prevent the formation of a complete game tree and require probabilistic analysis to evaluate positions. Unlike deterministic perfect-information games, where outcomes depend solely on player choices, these games adapt combinatorial theory through calculations and processes. Hidden information, meanwhile, arises when players lack full knowledge of the game state, leading to strategies that account for in opponents' positions or actions. Backgammon exemplifies a game with chance via rolls that determine moves, combined with strategic decisions amplified by the doubling cube, which allows players to double stakes and force opponents to accept or concede. The optimal use of the doubling cube relies on computing expected values; for instance, in a continuous model approximating , a player should double when their winning probability exceeds 80% and decline an opponent's double below 25%. This threshold balances the risk of loss against potential gains, with simulations confirming that deviations reduce long-term equity. Snakes and Ladders represents a pure chance game where player input is limited to rolling a die, and progress follows fixed paths altered by snakes (setbacks) and ladders (advances), analyzable as a . Each square forms a state, with transitions determined by die outcomes (each with probability 16\frac{1}{6}), snakes redirecting to lower states and ladders to higher ones; the absorbing state at the end yields the of paths to victory. For a standard 100-square board, the expected number of turns to win is approximately 39.2, highlighting the game's reliance on ergodic properties rather than skill. Battleship incorporates hidden information through concealed ship placements on a grid, where players alternate guesses to locate and sink opponents' fleets, drawing on for optimal probing. Strategies minimize expected misses by prioritizing high-probability regions; for example, after an initial hit, algorithms bound the minimum misses needed to sink a ship of length kk at most k−1k-1, using parity and adjacency to guide searches. This imperfect-information setup prevents exhaustive enumeration, as the opponent's configuration remains unknown until revealed. Poker variants, such as Texas Hold'em, feature both chance (card draws) and hidden information (private hands), where bluffing exploits uncertainty to represent stronger holdings than held. In simplified models, bluffing frequency equilibrates at the ratio of to ensure opponents are indifferent to calling, preventing exploitation; for a basic poker-like game, the optimal bluff probability is bb+c\frac{b}{b + c}, where bb is the bet size and cc the call cost. Without full lattices due to concealed cards, equilibrium strategies mix actions across information sets to balance ranges. Analysis of chance elements often employs dynamic programming to compute value functions over states, incorporating transition probabilities from random events like dice rolls. For instance, in -based games, the optimal maximizes the probability of reaching a before a fixed number of turns, solved via on expected utilities. Simulations complement this by estimating rare-event probabilities when exact computation is infeasible. For hidden information, strategies operate over information partitions—sets of indistinguishable histories—ensuring consistent actions within each to avoid signaling private knowledge. In probabilistic Nim variants, where heap reductions involve random removals, optimal play employs risk-neutral strategies that maximize expected heap values rather than minimizing variance. This approach aligns with equilibria, treating chance moves as neutral to risk in payoff calculations.

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  55. https://proceedings.neurips.cc/paper/2007/file/08d98638c6fcd194a4b1e6992063e944-Paper.pdf
  56. https://staff.fnwi.uva.nl/u.endriss/teaching/game-theory/slides/gt8.pdf
  57. https://arxiv.org/pdf/1901.04882
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