Network approach for decision making under risk—How do we choose among probabilistic options with the same expected value?

Wei Pan; Yi-Shin Chen

doi:10.1371/journal.pone.0196060

Abstract

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing ‘goal’ and ‘time’ factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight.

Citation: Pan W, Chen Y-S (2018) Network approach for decision making under risk—How do we choose among probabilistic options with the same expected value? PLoS ONE 13(4): e0196060. https://doi.org/10.1371/journal.pone.0196060

Editor: Lazaros K. Gallos, Rutgers The State University of New Jersey, UNITED STATES

Received: January 16, 2017; Accepted: April 5, 2018; Published: April 27, 2018

Copyright: © 2018 Pan, Chen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported from Ministry of Science and Technology, Taiwan in grant No. MOST-106-2221-E-007-115-MY2, MOST-106-3114-E-007-013, and MOST-105-2112-M-033-003.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Decision making under risk, i.e., all possible outcomes and the associated probabilities of the options are well known, has attracted the interests of researchers in a number of disciplines, including economics, psychology, neuroscience, and business, and has also inspired discourse on human’s rationality [1–10]. The expected utility theory established by von Neumann and Morgenstern [11] states that in a multiple choices problem, a rational decision is assumed to maximize the utility function (EU), the product of utility (u) and the associated probability (p). Many cases have shown that this criterion provides a good reference in decision making and can explain the behaviors of decision makers [3, 12–14].

However, the utility theory does not provides explanations for several behaviors in decision making. Several studies have also shown that a certain amount of people do not always take the option(s) with the maximum EU [15–21]. For instance, in experiments conducted by Kahneman and Tversky, given the choice between options with (u, p) of A(4000, 0.2) and B(3000, 0.25), 65% of the subjects took the former. However, between C(4000, 0.8) and D(3000, 1), 80% of the subjects took the latter. Here, the option (u, p) means that the subjects who take this option have a p chance of obtaining u and a (1 − p) chance of obtaining nothing. In other words, at least 45% of the overlapped subjects were inconsistent in responding to these two questions [22–27]. Kahneman and Tversky explained the psychological aspect of choosing D between C and D; in gain framing, as proposed in their prospect theory, these people were referred to as risk averse [17, 25, 28–30]. The framing effect introduced in prospect theory pointed out that people might count on different reference points in frames of gains and losses [31–33]. Moreover, people are often risk averse in decisions involving gains and are often risk seeking in decisions involving losses. These inconsistent behaviors owing to the shift of reference points in different frames are considered as irrational behaviors. Consistency is a principal aspect of the rational behaviors [34–39].

In evolution in the natural world, an act that is broadly disseminated through genes or memes should benefit, or at least not be harmful to, the actors or the offspring. In other words, an apparently detrimental action that is often observed should be beneficial in an obscure way [40]. The behavior of human beings, who are members of the ecological sphere, should also be confined by such a natural rule. Therefore, ‘inconsistent’ behaviors require a further study to find the hidden benefits and mechanisms.

Further, the applicability of the expected utility theory in decision making relies on the viability of maximizing the EU, which counts on the law of large numbers. That is, it requires a large number of people making a decision or a person making a decision a lot of times to obtain outcomes close to the expected value. However, for an individual who has aspirations other than the average, the results from many people would not be a good reference to make such a decision. The real outcome would deviate a lot from that predicted by the expected value [41–45]. Thus, new criteria other than maximizing the EU are needed.

Timing is significant as to satisfy one’s needs in time is an important issue in decision making [44–52]. With respect to time, people may have different needs in short-term or long-term. An individual may have different financial needs at different stage of life. Thus, one may need to set specific goals in the financial plans according to the ages [53]. Additionally, achieving a goal as soon as possible usually, but not always, fits the decision maker’s needs. For example, in an election campaign, the best strategy for the candidate is to reach the highest rate of support exactly on voting day in order to transform the support rate into votes−peaking too early or too late does not as effectively benefit the candidate. Thus, the reference points may change in different periods [54, 55].

In this study, we provide an alternative aspect by considering goals and the times in making decisions. It is assumed that the behaviors in decision making is meant to maximize the probability of achieving a goal during or by a designated time. As human beings have a planning nature, taking the goal and time, a concrete aim for the future, into account makes an individual’s activity meaningful [56].

This paper is organized as follows. First, a model that considers both goals and times is proposed; in this model, decisions are considered to be a process moving from the initial stage through several intermediate stages to ultimately reach the goal. These stages and the connecting paths are respectively illustrated as nodes and links in a network [57–59]. The decisions are a series of choosing a link that connects the current node to the next node until arriving to the node of goal. This process is equivalent to a walking on the constructed network, which is then further transformed into a matrix [60]. We then introduce strategies that set different weighting on choosing the available options. The outcomes of this series of decisions can be calculated by iteration from the continual multiplication of the matrices. Several strategies are raised to demonstrate how the proposed model works. Finally, we discuss the applications of the model in measuring the goal in mind and the mathematical foundation to the economic behaviors.

Methods

In our model, the decision process is divided into three parts, (1) Game set, (2) Strategy, and (3) Iteration. The first part, Game set depends on the situation in which the decision maker is involved including the number of options and the associated (u, p). Not losing generality, we set the utility as the monetary value. According to Game set, one can construct a network, which resembles the diagram in Markov decision process [61–63]. The second part, Strategy, is new that a decision maker is freely to distribute the weights on options as to narrowly or broadly bracket choices [64]. One can construct the nodes and links of the decision network according to these two parts. We can further transform the network into a matrix and proceed the third part, Iteration to find the time-dependent probability of achieving the goal, P_com(t). The decision maker can adjust Strategy to maximize P_com(t) during the designated t. In this model, the cost is the time spent on the game.

Game set: Construction of the network

Approaching mode and Growing mode are raised for constructing the network as shown in Figs 1 and 2, respectively.

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Fig 1. Construction of the network in a decision game of G of S₀ in Approaching mode.

(I) Payoff table: the decision maker who chooses option j has a chance of p_j to gain u_j or a chance of (1 − p_j) to gain nothing. (II) Game set: the game set according to the payoff table in (I) is further illustrated. At S_k, the decision maker has k options, e.g., option 1 leads to S_k−1 with a probability of p₁ or stay at S₀ with a probability of (1 − p_k−1), option 2 leads to S_k−2 with a probability of p₂ or stay at S₀ with a probability of (1 − p₂), …, and option k leads to S₀ with a probability of p_k or stay at S₀ with a probability of (1 − p_k). (III) Weights: the decision maker at S_k can distribute the weights of ^kw₁, ^kw₂, …, and ^kw_k on the options of (u₁, p₁), (u₂, p₂), …, and (u_k, p_k), respectively. Thus, the probabilities of moving from S_k to S_k−1, S_k−2, …, and S₀ are ^kw₁ p₁, ^kw₂ p₂, …, and ^kw_k p_k, respectively. The one also has a probability of (^kw₁(1 − p₁) + ^kw₂(1 − p₂) + … + ^kw_k(1 − p_k)) of staying at S_k.

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Fig 2. Construction of the network in a decision game of G of S₀ in Growing mode.

(I) Payoff table: the decision maker who chooses option j has a chance of p_j to gain u_j or a chance of (1 − p_j) to gain nothing. (II) Game set: the game set according to the payoff table in (I) is further illustrated. At S₀, the decision maker has k options, e.g., option 1 leads to S₁ with a probability of p₁ or stay at S₀ with a probability of (1 − p₁), option 2 leads to S₂ with a probability of p₂ or stay at S₀ with a probability of (1 − p₂), …, and option k leads to S_k with a probability of p_k or stay at S₀ with a probability of (1 − p_k). (III) Weights: the decision maker at S₀ can distribute the weights of ⁰w₁, ⁰w₁, …, and ⁰w_k on the options of (u₁, p₁), (u₂, p₂), …, and (u_k, p_k), respectively. Thus, the probabilities of moving from S₀ to S₀, S₁, S₂, …, and S_k−1 are ⁰w₁ p₁, ⁰w₂ p₂, …, and ⁰w_k p_k, respectively. The one also has a probability of (⁰w₁(1 − p₁) + ⁰w₂(1 − p₂) + … + ⁰w_k(1 − p_k)) of staying at S₀.

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The payoff table of the decision game for the Approaching mode is listed in Fig 1(I). The decision maker who choose option j has a p_j chance of obtaining u_j that move j stages and a (1 − p_j) chance of staying at the original stage. In the Approaching mode as shown in Fig 1(II), the stage occupied by the decision maker is denoted as S_k, indicating that it requires k stages to reach the goal, S₀. Thus, a series of decisions is considered a walk from S_k to S₀ in the network. The available options correspond to the links connecting to the following nodes according to the payoff table. For a decision maker at S_k, option i indicates an outward link pointing to S_k−i and a returning link associated with probabilities of p_i and (1 − p_i), respectively. The value and the utility of u_i is set to be equal to move i stages in our research.

In the Growing mode as shown in Fig 2(II), the initial stage is denoted as S₀. The options taken by the decision maker correspond to the links connecting to the following nodes according to the payoff table as shown in Fig 2(I). At S₀, option i indicates an outward link pointing to S_i and a returning link associated with probabilities of p_i and (1 − p_i), respectively. Again, the value and the utility of u_i is set to be equal to move i stages.

Strategy matrix, W

The decision maker can distribute weights on the possible links (options). In the Approaching mode as shown in Fig 1(III), the decision maker can distribute a weight, ^kw₁ (< 1), on option i when she is at S_k. Thus, she has a ^kw_i p_i chance of moving to S_k−i and a ^kw_i(1 − p_i) chance of staying at S_k. Generally, at S_k, the decision maker can set the weights of ^kw₁, ^kw₂, … ^kw_k−1, and ^kw_k on the options 1, 2, …, k − 1, and k, respectively. Thus, the chances of moving from S_k to S_k−1, S_k−2, …S₁, and S₀ are ^kw₁ p₁, ^kw₂ p₂, … ^kw_k−1 p_k−1, and ^kw_k p_k, respectively. The chance of staying at S_k is (^kw₁(1 − p₁) + ^kw₂(1 − p₂) + …^kw_k−1(1 − p_k−1) + ^kw_k(1 − p_k)), i.e. ^kw_i(1 − p_i).

In the Growing mod as shown in Fig 2(III), the decision maker can distribute a weight, ⁰w_i (< 1), on option i when she is at S₀. Thus, she has a ⁰w_i p_i chance of moving from S₀ to S_i and a ⁰w_i(1 − p_i) chance of staying at S₀. Generally, at S₀, the decision maker can set the weights of ⁰w₁, ⁰w₂, … ⁰w_k−1, and ⁰w_k on the options 1, 2, …, k−1, and k, respectively. Such that, the chances of moving from S₀ to S₁, S₂, …S_k−1, and S_k are ⁰w₁ p₁, ⁰w₂ p₂, … ⁰w_k−1 p_k−1, and ⁰w_k p_k, respectively. The chance of staying at S₀ is (⁰w₁(1 − p₁) + ⁰w₂(1 − p₂)+ … ⁰w_k−1(1 − p_k−1) + ⁰w_k(1 − p_k)), i.e. ⁰w_i(1 − p_i).

Since the outward links are conservative, the total of the weights is set as 1, i.e., ^jw_i = 1 for any j in both modes. A strategy in a decision corresponds to how the decision maker distribute the weights, ^jw_i, on the available links at S_j. For simplicity, we take Approaching mode in the following sections. A decision maker at S_i distributes the weight on the link, (u_m, p_m) and is denoted as ⁱw_m, which indicates moving m stages to S_i−m with a probability of p_m or staying at S_i with a probability of (1 − p_m). A strategy matrix W is set when all elements, b_ij are set, which are equivalent to ⁱw_i−j.

Matrices

The network of a decision game can be transformed into a game matrix Q [60]. The Q is a (k + 1) × (k + 1) matrix with the elements, q_ij are the probability connecting S_i to S_j. Additionally, the distribution of the weights on each option can be represented in a strategy matrix W. In our game setting, Q is determined by the decision game and W depends on the decision maker’s strategy. They are mutually independent. The Q and the W can be further composed into a matrix A. The correlation of these matrices are shown in Eq 1, in which “∘” denotes the operation of the Hadamard product, a_ij = q_ij ⋅ b_ij for i > j. The diagonal elements of A are set in D, which contains only diagonal elements, i.e., d_ii = a_ii and d_ij = 0 for i ≠ j. (1)

Walking on the network can be further transformed into an iteration of a transition matrix, A. The element a_ij indicates the transition probability from S_i to S_j [60]. Thus, A is a (k + 1) × (k + 1) matrix with elements indexing from a₀₀ to a_kk. Those a_ij are i ⁱw_(i−j) p_(i−j) for i > j. The diagonal elements, a_ii corresponding to the self-linking links, are ⁱw_m(1 − p_m). The left part of A and the associated elements a_ij are shown in Eqs 2 and 3, respectively. (2) (3) (4)

The sum of each row of A is 1 as shown in Eq 4, indicating that A is not only a lower triangular but also a stochastic matrix.

Iteration: State vector and iteration relation

We then introduce a state matrix, R(t), [r₀, r₁, r₂, …, r_k] with dimension of 1 × (k + 1). The elements r_i(t) indicate the probability of a decision maker staying at S_i in t. Therefore, a decision maker begins at S_k, which corresponds to R(0) of [0, 0, 0, …, 1]. The first element, r₀(t), is called P_com(t), the probability that a decision maker arrives at S₀ within t. The next state, r₀(t + 1), is generated from the product of r₀(t) and a_ij as shown in Eq 5. Thus, the state matrix R(t) fits the iteration relation as shown in Eq 6. The decision maker eventually arrives at S₀, which corresponds to R(∞), [1, 0, 0, …, 0]. (5) (6)

We then examine the solvability to find the best strategy to satisfy the decision maker. In a decision game beginning at S_k, the number of unknown variables in W is k(k + 1)/2. There are (k + 1) constraints including the sum of each row of W and the desired . There are still k(k + 1)/2 − (k + 1) undetermined variables. The problem can be analytically solved when k is less than 2. For k > 2, one can construct a trail W and then obtain the r₀(t) resulting from the iteration relation in Eq 6. The decision maker may try several Ws in order to determine strategies that satisfy the needs.

Strategy comparison

At S_k, the decision maker has k number of options from (u₁, p₁), (u₂, p₂), (u₃, p₃), …, to (u_(k−1), p_(k−1)), and (u_k, p_k) linking to S_(k−1), S_(k−2), S_(k−3), …, to S₁, and S₀, respectively. For resolving the ambiguity raised in Abstract, the expected utility for each option is set as 1, i.e., (u_i, p_i) is set as linking from S_k to S_k−i. This setting is also for comparison of different strategies choices among options without privilege. In this paper, we raise strategy examples: fix, min k, max k, risky, random, and safe.

The fix strategy indicates that a decision maker always chooses a certain option until the end of the game as shown in Fig 3. Assuming that the decision maker takes the option of (m, p_m), only g/m stages are needed to arrive at S₀.

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Fig 3. The network of the fix strategy choosing (m, p_m).

For simplicity, the G is set as 3m to demonstrate the network. The decision maker only passes the stages with indexes of (g − nm), where m = g/u and n = 0, 1, 2, or 3, i.e., 3 stages from S_g to S₀. That is, there are only four stages with this strategy: S_g, S_g−m, S_g−2m, and S₀ (or S_3m, S_2m, S_m, and S₀, respectively).

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A decision maker taking the min k strategy always chooses the safest option, i.e., (1, 1). In other words, the decision maker moves one stage each time. In a game with a goal (G) of g, it takes g stages to reach G. This provides us a baseline for comparison with other strategies. With the max k strategy, the decision maker always chooses the riskiest option, i.e., the one with the highest utility, (g, 1/g), as shown in Fig 4. The decision maker either stays at the very beginning, S_g, or moves to S₀ with a probability of (1 − 1/g) or 1/g, respectively. Both min k and max k are special cases of the fix strategy. With the risky strategy, the decision maker places more weight on the high utility options. In other words, at any stage during the game, S_k, she chooses the option of (k, 1/k) in a probabilistic manner, which is in proportion to the utility, k. With the safe strategy, the decision maker places more weight on the options with high probability. That is, she chooses the option of (k, 1/k) at S_k in a probabilistic manner, which is in proportion to the probability, 1/k. With the random strategy, the decision maker chooses the option in a random manner in all stages. That is, she weights all option equally. The Ws for the strategies, risky, random, and safe are listed in Table 1. Note that the real time can be estimated by multiplying t with an approximated period τ to make a decision, i.e., tτ.

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Fig 4. The network of the max k strategy in a decision game of G of g.

The decision maker always chooses the option with the highest utility, i.e., (g, 1/g), a special case of the fix strategy. Thus, she either stays at S_g or moves to S₀. The intermediate stages, S_g−1 to S₁, are not reachable.

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Table 1. Strategies in a decision game at S_i.

Pr: (k, 1/k) is the probability to choose (k, 1/k).

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The results are calculated by the iteration relation, as shown in Eq 6, to see how r₀(t) evolves with the associated W. It can also be performed by computer simulation. Because of the law of large numbers, both methods should produce consistent outcomes. Therefore, we only show the results from the calculations.

Results

The results are presented both in the time-dependent cumulative distribution function (CDF, P_com(t)) of the probability of achieving the goals and the associated time-dependent probability mass function (PMF, P(t)).

Fixed option strategy

We first show the outcomes of the fix strategy. In a decision game with G of g, the node number is reduced from (g + 1) to when the decision maker takes (m, p_m) only, as shown in Fig 3. The dimension of the associated matrix A is also reduced from (g + 1) × (g + 1) to . Thus, there are only two outward links for each stage: the one linking to the next stage and the other linking to the original stage. The associated A becomes a band matrix in which the non-zero elements locate only in the main diagonal and the first diagonal below, as shown in Eq 7. (7)

The networks of the fix strategies with u of 4 and 6 in a decision game of G of 24 are shown in Fig 5A and 5B, respectively. For the decision maker that chooses C(4, 0.5), the network becomes a six-stage link from S₂₄ to S₀ through S_4n, where n ∈ integers. For the decision maker that chooses B(6, 0.3), the network becomes a four-stage link from S₂₄ to S₀ through S_6n, where n ∈ integers. From the aspect of networks, the decision involves comparing a six-stage route with higher probability of moving to a four-stage route with lower probability of moving.

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Fig 5. The network of the a decision game of G of 24 with options of

(or B(6, 0.3)) and C(4, 0.5).

The network can be decoupled when a decision maker takes the fix strategy. For a decision maker that chooses A (or B), the network becomes a four-stage network linking from S₂₄ to S₀ through S₁₈, S₁₂, and S₆. For a decision maker that chooses C, the network becomes a six-stage network linking from S₂₄ to S₀ through S₂₀, S₁₆, S₁₂, S₈, and S₄.

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The game shown in Fig 5 is taken as an example for comparisons of (1) options with the same expected utility, and C(4, 0.5), and, (2) options with different expected utilities, B(6, 0.3) and C(4, 0.5). In Fig 6B, the CDF curves of options of A and C interlace at t_int of 12. This indicates that on average, both options take 12 stages to achieve the goal because the expected utility for both is 2. In the region of t < t_int, P_comA is larger than P_comC, which indicates that A is more preferable than C in this region. The situation is reversed in the region of t > t_int, where P_comC is larger than P_comA, indicating that option C is more preferable than A. The result demonstrates that options with the same expected utilities lead to different probabilities of achieving the goal. The riskier option A leads to a higher probability of achieving the goal in the earlier period, whereas the safer option C leads to a higher probability of achieving the goal in the later period. This is also found in the PMF curves that the peak positions are in the order of A < C in Fig 6A.

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Fig 6. Comparison of the fix strategies with different expected utilities in a decision game of G of 24.

The options are , B(6, 0.3), and C(4, 0.5), denoted by the black square, red circle, and blue diamond, respectively. The PMF and CDF curves are shown in (A) and (B), respectively. Between A and B, where ΔuΔp > 0, it is found that choosing A is dominant because P_comA > P_comB in the whole t range shown in (B). Between A and C, which have the same expected utility, the two curves interlace at t_int near 12. For t < t_int, A is preferred, whereas for t > t_int, B is preferred. Between B and C, which have different expected utilities, the two curves interlace at t_int near 10. For t < t_int, B is preferred, whereas for t > t_int, C is preferred. Therefore, between the options in which ΔuΔp < 0, the preferred option becomes t dependent.

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By introducing the goal, the ambiguity of choosing the options with the same expected utilities is eliminated. In other words, a decision maker willing to achieve an urgent goal can take the option with a larger u, i.e., a risky option; a decision maker willing to achieve a goal in the future can take the option with larger p, i.e., a safe option.

We compare the results of choosing the options with different expected utilities, B(6, 0.3) and C(4, 0.5) in Fig 6. The two CDF curves of B and C interlace at t_int near 10 in (B). For t < t_int, that P_comB is larger than P_comC indicates B is preferred in the earlier region. For t > t_int, that P_comC is larger than P_comB indicates C is preferred in the later region. Again, taking option C, which has a higher expected utility, is preferable for a decision maker willing to take a longer time to achieve her goal. However, for a decision maker wanting to achieve her goal in a shorter period, taking B is preferable, even though the corresponding expected utility is low. This might provide a mathematical explanation for the behavior of choosing the option with a low expected utility, which can be interpreted as the decision maker wishing to satisfy her desire quickly.

Comparing the CDF curves of A and B in (B), it is found that P_comA is larger than P_comB in the whole t range investigated. The ambiguity in decision making comes from when ΔuΔp < 0 ((u_i − u_j)(p_i − p_j) < 0). This also results an interlace in the CDF curves. In other words, there exists a dominant option when p_i ⩾ p_j or u_i ⩾ u_j and ΔuΔp > 0. The criterion of the preferable option using the fix strategy can be formulated as Eq 8. (8)

Baseline for comparison: The min k strategy

A decision maker who takes the min k strategy always chooses the option of (1, 1) despite the stage at which she steps in. It takes g stages to move from S_g to S₀ since this option consistently leads the decision maker to advance one stage each time. The P as a function of t for this strategy is a delta function with non-zero value at g and the associated P_com is a step function with a value of 1 for t ⩾ g, as shown in the black lines in Fig 7A, 7B and 7C. This indicates that the decision maker will not reach the goal until t = g. This can serve as a baseline for comparison with the other strategies.

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Fig 7. The CDF (P_com, upper panel) and PMF (P, lower panel) curves for the strategies of max k (grey hexagon), risky (black square), random (red circle), safe (blue diamond), and min k (black line) to reach the goals of (A) 10, (B) 50, and (C) 100.

The P_com curves interlace near t_int of 12, 60, and 120 for (A), (B), and (C), respectively. In the regime of t < t_int, the P_com are in the order of max k, risky, random, safe, and min k, indicating that the max k strategy leads to the highest probability of achieving a goal as soon as possible. However, in the regime of t > t_int, the P_com are in reverse order, indicating that the min k strategy leads to the highest probability of achieving the goal at a later time. The averages of all these strategies in (A), (B), and (C) are 10, 50, and 100, respectively.

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The riskiest decision maker: The max k strategy

The max k strategy can be considered a special case of the fix strategy where the riskiest option is always taken. The network for the max k strategy in a game with G of g is illustrated in Fig 4. The associated matrix is shown in Eq 9. The state R is a 1 × 2 row vector with an initial state R(0) of [0, 1], as shown in Eq 10. The P_com can be deduced to a function of t, as shown in Eq 11. The outcome of this strategy is shown in the following section. (9) (10) (11)

Comparison of the strategies

The CDF and the PMF as functions of t for the exemplified strategies in decision games with G of 10, 50, and 100 are shown in Fig 7A, 7B and 7C, respectively.

The outcomes exhibit scalability in that the positions of the peaks of the curves for the strategies investigated are in proportion to the G. For example, the peaks of the curves for the safe strategy are at 10, 50, and 100 for the G of 10, 50, and 100, respectively. Therefore, we will only describe the results from Fig 7A in details as the other two results for G of 50 and 100 exhibit the same qualitative properties.

The CDF curves are shown in the upper panels. The curves for max k, risky, random, and safe interlace at t_int near 12, 60, and 120 for G of 10, 50, and 100, respectively, which also show scalability. In the range of t < t_int, the P_com are in the order of max k > risky > random > safe > min k. This result indicates that the riskier strategies lead to a higher probability of achieving the goal by t_int. However, in the range of t > t_int, the order is reversed. This indicates that the safer strategies have a higher probability of achieving the goal in the long run.

The PMF curves for the exemplified strategies are very different although the expected utility for the options are all the same. The peak position indicates that the majority of decision makers who take that strategy achieve their goal during that period. Among these strategies, the peak positions are in the order of max k < risky < random < safe ≃ min k as long as the t increases. This provides suggestions for decision makers who wish to achieve a goal during a designated period. With a G of 10, for example, for decision makers who wish to achieve their goals during 8 < t < 12, the safe strategy is recommended, whereas for those wish to achieve their goal during 4 < t < 10, the risky strategy is recommended.

In our game set, the expected utilities for all options are the same, as are the resulting average times to reach the goal. This is also plausible according to the law of large numbers. Thus, in the CDF curves shown in Fig 7, the strategy that leads decision makers to a higher probability of achieving a goal in an early period also leads them to a lower probability of achieving the goal in a late period. Therefore, a decision maker can set strategies, i.e., construct the W and apply our method to find satisfactory strategies according to the resulting P_com(t) and P(t).

Discussions

Expected utility revisited

No matter which strategy a decision maker takes, the average t to reach the goal is the same, which is also guaranteed by the law of large numbers. However, the resulting distribution of P_com(t) is highly dependent on the various strategies. By taking the goal and time into account, our results show that the expected utility is not the sole criterion, even though it is often used in evaluating strategies in decision making under risk. For a decision maker who wants to achieve a goal as soon as possible, riskier options are preferable than safer options, even though the former may have a lower expected utility. Therefore, the criteria for a rational decision need to be reconsidered.

Rationality often implies that a decision maker should be consistent. In other words, a decision maker who takes the option with the maximum expected utility in this question, should also take the one with the maximum expected utility in another question. According to our results, this inconsistency might imply that people do not maximizing the expected utility, but the P_com(t). That is, in some circumstances, a decision maker may wish to accomplish a goal earlier, and in other circumstances, she may wish to accomplish a goal later. Furthermore, goals might vary under different circumstances. Inconsistency in answering the decision question might not exhibit the irrationality, but rather reveal a different goal-time consideration. Therefore, the meaning of rational choice needs to be redefined.

Measurement of goal in mind

In our model, a decision maker should consider both the goal and time when choosing a strategy, as well as the distribution of the weights on options. As in the results shown in Fig 6, a decision maker who chooses a riskier option with a low expected utility might reveal urgency in achieving her goal. This might provide an opportunity to measure the decision maker’s wishes and also shed light on revealing the automatic and unconscious calculation in mind. It is suggested that an individual’s series of decisions be recorded in order to estimate their goal and urgency in achieving it. The goal in mind might be measured when the time question is answered, e.g., by the individual’s plan. The urgency might be obtained when the goal in mind is answered directly by the individual or estimated according to the individual’s need.

The aforementioned experiments conducted by Kahneman and Tversky [22, 31, 65, 66] are taken as an example to illustrate how to estimate the goal or urgency. Fig 8 shows the CDF curves for a decision between A(4000, 0.2) and B(3000, 0.25) and between C(4000, 0.8) and D(3000, 1) with G of 24000 (upper panel) and 12000 (lower panel). Accordingly, 65% of the subjects chose A between A and B, and 80% chose D between C and D. Our model provides a possible explanation for the majority of subjects who took A and D. We then try to find a range of t that satisfies both P_comA > P_comB and P_comD > P_comC. If they wished to achieve the goal within 5 < t < 20 (10 < t < 42), a goal in mind could be estimated as 12000 (24000). On the other hand, if they stated that their goal were 12000 (24000), the t in which they wished to reach the goal could be estimated as 5 < t < 20 (10 < t < 42).

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Fig 8. The CDF curves for decisions between A(4000, 0.2) and B(3000, 0.25), and between C(4000, 0.8) and D(3000, 1) with G of 24000 (upper panel) and 12000 (lower panel).

A possible explanation resulted from our model − the majority of subjects that took A and D might imply that they wish to achieve a goal of 12000 within 5 < t < 20 or to achieve a goal of 24000 within 10 < t < 42. If these subjects wish to achieve the goal by t = 30, it could be estimated that the goal in mind is about 24000.

https://doi.org/10.1371/journal.pone.0196060.g008

Prospect theory revisited

By using our model, we try to reinterpret two aspects in prospect theory [22, 24–26, 31]: (1) the framing effect and (2) non-linear preference. The framing effect, as mentioned previously, describes that in frame of gains or losses, people tend towards risk aversion or risk seeking, respectively.

In a decision game with the payoff table as shown in Table 2. A decision maker that is s stages away from S₀ has two options: a riskier option, R(u_r, p_r), and a safer option, S(u_s, p_s), in which u_r p_r = u_s p_s and p_r < p_s. In the frame of gains as shown in Fig 9A, a decision maker is s stages away from S₀. Choosing either R or S may lead her to S₀. However, choosing S leads her to S₀ with a higher chance than that choosing R. Besides, choosing R results in a probability of (1 − p_r) to stay at S_s, which is higher than (1 − p_s) that choosing S. For a decision maker at the stage near the goal, it is preferable to choose a safer option. Therefore, one would exhibit risk aversion because there is no need to choose a riskier option with a small probability and a large reward that is in excess of the goal. In an extreme case, it is preferable to choose an option of a sure gain when the gain can just lead her to the goal with certainty.

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Fig 9. Examples of the mathematical interpretation of the economic behaviors in (A) frame of gains and (B) frame of losses.

There are two options, a riskier R(u_r, p_r) and a safer S(u_s, p_s), in which u_r p_r = u_s p_s and p_r < p_s. (A) In the frame of gains, both u_r and p_s are positive. When the decision maker is at S_s, which is s stages away from the goal, S₀, both options may lead her to S₀. Nevertheless, choosing S allows her a higher probability to arrive S₀ and a lower probability to stay at S_s than choosing R. Thus, S is preferable. This demonstrates a risk aversion behavior for a decision maker when the positive goal is near in the frame of gains. (B) In the frame of losses, both u_r and u_s are negative. When the decision maker is at S_s, which is s stages away from S₀ that she tries to avoid. Both options may lead her to S₀. However, choosing R allows her to stay at S₀ with a higher probability than choosing S ((1 − p_r) > (1 − p_s)). Although choosing R may lead her to S_s−r, a worse stage than S₀, it does not really matter since both S_s−r and S₀ indicate her arrival to the negative goal. Additionally, choosing R may lead her farther away from S₀. This demonstrates a risk seeking behavior for a decision maker when the goal is near in the frame of losses.

https://doi.org/10.1371/journal.pone.0196060.g009

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Table 2. Reward and probability.

https://doi.org/10.1371/journal.pone.0196060.t002

We further consider the situation in frame of losses by assuming both u_r and u_s are negative. In Fig 9B, S₀ is set as negative, indicates a lower bound of losses, e.g., the bankruptcy, which a decision maker tries to avoid. At S_s, choosing R results in a chance of (1 − p_r) to stay at S_s, which is higher than (1 − p_s) that choosing S. Therefore, the decision maker would show a preference in choosing a riskier option. That is, a decision maker may behave as a risk seeker in the frame of losses, especially when she is near the negative goal. Besides, a sure loss (p_s = 1) is not welcome since a rational decision maker tries to avoid the arrival at S₀.

This demonstrates risk aversion in frame of gains and risk seeking in frame of losses. Such an asymmetry between the behaviors in the frame of gains and the frame of losses is embedded in our model. In other words, the framing effect in prospect theory can be mathematically interpreted.

The nonlinear preference, the concavity and convexity in the value-utility curve as shown in the prospect theory is referred to psychological activities [25]. In our model, the expected utility is in proportion to the monetary value. Hence, the criterion instead of the expected utility, P_com(t) is intrinsically nonlinear, which may related to the nonlinear preference in the prospect theory.

Mathematical interpretation of economical behaviors

The house money effect proposed by Thaler and Johnson [28] describes that people will tend to spend the money of a prior gain, e.g., earned from gambling, in a risk seeking mode. Our model can interpret that the one has a larger goal in dealing with this money. In other words, the one is willing to take a risk because the goal is out of the consideration. In the presence of prior losses, the decision maker will take the options with rewards that have opportunities to break even, which is called break—money effect [28]. This implies that with a prior loss, the goal might be shifted to break even or at least get something [65, 67, 68]. Such that, the tendencies toward a risk-seeking behavior would be enhanced especially in the frame of losses.

Besides, people may divide their money in parts, which will be used in different ways according to mental accounts [28]. Our model suggests that people can finely adjust the weights in a strategy for each account by the goal and the time to reach that goal. The computational shortcut is proposed to be included in heuristic procedure in decision making [8, 25, 69–72]. Our model suggests that people might unconsciously proceed such a mental computation on the P_com.

Perspectives

An individual’s behavior or activity may play a crucial role in the collective cooperation. It is proven that the social diversity of individual behavior will substantially improve the level of cooperation [73–76]. Presently, our model can be considered as a single player game, which provides a reference for the behaviors of individuals in decision making. Such that, the game theory can involve by considering the players’ goals and urgencies based on our model. Additionally, the policy maker may design a game situation to enhance the level of cooperation in order to obtain the public good with less contradiction.

For making decisions under uncertainty, where the (u, p) for each option is unknown. Learning from experience is thus important in constructing the (u, p) practically and in explaining the human behaviors [55, 77]. Our model provide a mathematical process to estimate the (u_i, p_i) for all possible options. As shown in Eq 6, A can be resolved by the resulting P_com(t) by setting a trial W after repeated decision-makings. This provides an opportunity to find the decision network, possible combinations of the (u_i, p_i)s, and the associated Qs through these trials. Therefore, learning from experiences becomes mathematically achievable.

The proposed model combines “the goal and time” together to provide a mathematical procedure in decision making and also provides a mathematical foundation to explain several psychological effects in economical behaviors. Additionally, such a mathematical procedure also provides foundation for decision making by Artificial Intelligence.

Conclusion

The proposed network approach provides a powerful tool for analysing the time-dependent outcomes of decisions. The behaviors that contradict those predicted by expected utility theory can be mathematically explained. Our results imply that planning could be embedded in decision behaviors. For a rational decision under risk, one needs to consider achievement of the desired goal within a specific period in the future rather than maximizing the expected utility in the present. Moreover, the model not only provides a mathematical foundation for resolving ambiguity in decision making, but also provides mathematical reasoning on the behaviors that chooses the options with lower expected utilities.

Acknowledgments

Wei Pan thanks Prof. Sai-Ping Li, Institute of Physics, Academia Sinica, Taiwan and Prof. Li-Yi Hsu, Department of Physics, Chung Yuan Christian University, Taiwan for the inspiring discussions. The authors thank the support from Ministry of Science and Technology, Taiwan in grant No. MOST-106-2221-E-007-115-MY2, MOST-106-3114-E-007-013, and MOST-105-2112-M-033-003.

References

1. Bermúdez JL. 1. In: Decision Theory and Rationality. Oxford University Press; 2009. p. 1–5.
2. Alchian AA. Uncertainty, Evolution, And Economic Theory. J Polit Econ. 1950;58(3):211.
- View Article
- Google Scholar
3. Frank RH. If Homo Economicus Could Choose His Own Utility Function, Would He Want One with a Conscience? Am Econ Rev. 1987;77(4):593.
- View Article
- Google Scholar
4. Opaluch JJ, Segerson K. Rational Roots of “Irrational” Behavior: New Theories of Economic Decision-Making. Northeastern Journal of Agricultural and Resource Economics. 1989;18(2):81.
- View Article
- Google Scholar
5. Bechara A, Damasio H, Tranel D, Damasio AR. Deciding Advantageously Before Knowing the Advantageous Strategy. Science. 1997;275 (5304). pmid:9036851
- View Article
- PubMed/NCBI
- Google Scholar
6. McFadden D. Rationality for Economists? J Risk Uncertainty. 1999;19(1-3):73.
- View Article
- Google Scholar
7. Slovic P, Finucane ML, Peters E, MacGregor DG. Risk as Analysis and Risk as Feelings: Some Thoughts about affect, reason, risk, and rationality. Risk Anal. 2004;24(2):311. pmid:15078302
- View Article
- PubMed/NCBI
- Google Scholar
8. Lee D. Neural basis of quasi-rational decision making. Curr Opin Neurobiol. 2006;16:191. pmid:16531040
- View Article
- PubMed/NCBI
- Google Scholar
9. d’Acremont M, Bossaerts P. Neurobiological studies of risk assessment: A comparison of expected utility and mean–variance approaches. Cogn Affect Behav Neurosci. 2008;8:363. pmid:19033235
- View Article
- PubMed/NCBI
- Google Scholar
10. Roiser JP, de Martino B, Tan GCY, Kumaran D, Seymour B, Wood NW, et al. A Genetically Mediated Bias in Decision Making Driven by Failure of Amygdala Control. J Neurosci. 2009;29(18):5985. pmid:19420264
- View Article
- PubMed/NCBI
- Google Scholar
11. von Neumann J, Morgenstern O. 1. In: Theory of Games and Economic Behavior. 3rd ed. Princeton University Press; 1980. p. 1–45.
12. Hey JD, Orme C. Investigating Generalizations of Expected Utility Theory Using Experimental Data. Econometrica. 1994;62(6):1291–1326.
- View Article
- Google Scholar
13. Rostain T. Educating Homo Economicus: Cautionary Notes on the New Behavioral Law and Economics Movement. Law & Soc’y Rev. 2000;34(4).
- View Article
- Google Scholar
14. Hooker C. Rationality as effective organisation of interaction and its naturalist framework. Axiomathes. 2011;21:99.
- View Article
- Google Scholar
15. Allais M. Le Comportment de l’homme Rationale Devant le Rsique, Critiques des Postulates et Axiomes de l’ecole Americaine. Econometrica. 1953;21:503.
- View Article
- Google Scholar
16. Conlisk J. Three variants of the Allais example. Amer Econ Rev. 1989;79:392.
- View Article
- Google Scholar
17. Denes-Raj V, Epstein S. Conflict between intuitive and rational processing: When people behave against their better judgment. J Pers Soc Psychol. 1994;66:819.
- View Article
- Google Scholar
18. Wiseman RM, Gomez-Mejia LR. A behavioral agency model of managerial risk taking. Acad Manage Rev. 1998;23:133.
- View Article
- Google Scholar
19. Mongin P. Does optimization imply rationality? Synthese. 2000;124:73.
- View Article
- Google Scholar
20. Abdellaoui M, L’Haridon O, Paraschiv C. Experienced vs. Described Uncertainty: Do We Need Two Prospect Theory Specifications? Manag Sci. 2011;57(10):1879.
- View Article
- Google Scholar
21. Nagengast AJ, Braun DA, Wolpert DM. Risk-sensitivity and the mean-variance trade-off: decision making in sensorimotor control. Proc R Soc B. 2011;278(1716):2325. pmid:21208966
- View Article
- PubMed/NCBI
- Google Scholar
22. Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk. Econometrica. 1979;47:263.
- View Article
- Google Scholar
23. Kahneman D, Knetsch JL, Thaler R. Fairness as a Constraint on Profit Seeking: Entitlements in the Market. Am Econ Rev. 1986;76(4):728.
- View Article
- Google Scholar
24. Tversky A, Kahneman D. Loss aversion in riskless choice: A reference-dependent model. Q J Econ. 1991;106:1039.
- View Article
- Google Scholar
25. Tversky A, Kahneman D. Advances in prospect theory: Cumulative represtation of uncertainty. J Risk Uncertain. 1992;5:297.
- View Article
- Google Scholar
26. Kahneman D, Tversky A. Choices, Values, and Frames. Cambridge University Press; 2000.
27. Kahneman D, Krueger AB, Schkade D, Schwarz N, Stone AA. Would you be happier if you were richer? A focusing illusion. Science. 2006;312:1908. pmid:16809528
- View Article
- PubMed/NCBI
- Google Scholar
28. Thaler RH, Johnson EJ. Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice. Manag Sci. 1990;36:643.
- View Article
- Google Scholar
29. Martino BD, Kumaran D, Seymour B, Dolan RJ. Frames, biases, and rational decision-making in the human brain. Science. 2006;313:684. pmid:16888142
- View Article
- PubMed/NCBI
- Google Scholar
30. Birnbaum MH. New Paradoxes of Risky Decision Making. Psychol Rev. 2008;115(2):463.
- View Article
- Google Scholar
31. Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science. 1981;211:453. pmid:7455683
- View Article
- PubMed/NCBI
- Google Scholar
32. Druckman JN, McDermott R. Emotion and the framing of risky choice. Polit Behav. 2008;30:297.
- View Article
- Google Scholar
33. Gächter S, Orzen H, Renner E, Starmer C. Are experimental economists prone to framing effects? A natural field experiment. J Econ Behav Organ. 2009;70:443.
- View Article
- Google Scholar
34. Good IJ. Rational Decisions. J R Statist Soc B. 1952;14(1):107.
- View Article
- Google Scholar
35. Davidson P. Rational expectations: a fallacious foundation for studying crucial decision-making processes. J Post Keynesian Econ. 1982-83;5(2):182.
- View Article
- Google Scholar
36. Bausor R. The Limits of Rationality. Social Concept. 1985;2:66.
- View Article
- Google Scholar
37. Anand P. The Nature of Rational Choice and The Foundation of Statistics. Oxford Economic Papers, New Series. 1991;43(2):199.
- View Article
- Google Scholar
38. Bourgeois-Gironde S, Giraud R. Framing effects as violations of extensionality. Theory Dec. 2009;67:385.
- View Article
- Google Scholar
39. Kirman A, Livet P, Teschl M. Rationality and emotions. Phil Trans R Soc B. 2010;365:219.
- View Article
- Google Scholar
40. Dawkins R. 11. In: The Selfish Gene. 30th ed. Oxford University Press; 2006. p. 189–201.
41. Samuelson PA. Risk and Uncertainty: A Fallacy of Large Numbers. Scie. 1963;98:108.
- View Article
- Google Scholar
42. Tversky A, Bar-Hillel M. Risk: The Long and the Short. J Exp Psychol Learn Mem Cogn. 1983;9(4):713.
- View Article
- Google Scholar
43. Redelmeier DA, Tversky A. Discrepancy between medical decisions for individual patients and for groups. N Engl J Med. 1990;322(16):1162. pmid:2320089
- View Article
- PubMed/NCBI
- Google Scholar
44. Benartzi S, Thaler RH. Risk aversion or myopia? Choices in repeated gambles and retirement investments. Manag Sci. 1999;45(3):364.
- View Article
- Google Scholar
45. Mallpress DEW, Fawcett TW, Houston AI, McNamara JM. Risk Attitudes in a Chaging Environment: An Evolutionary Model of the Fourfold Pattern of Risk Preferences. Psychol Rev. 2015;122(2):364. pmid:25844877
- View Article
- PubMed/NCBI
- Google Scholar
46. Lopes LL. Notes, Comments, and New Findings Decision Making in the Short Run. J Exp Psychol Hum Learn. 1981;7(5):377.
- View Article
- Google Scholar
47. Lopes LL. When Time is of the Essence: Averaging, Aspiration, and the Short Run. Organ Behav Hum Decis Process. 1996;65(3):179.
- View Article
- Google Scholar
48. Fried Y, Slowik LH. Enriching Goal-seeting theory with time: an integrated approach. Acad Manage Rev. 2004;29(3):404.
- View Article
- Google Scholar
49. Aloysius JA. Decision making in the short and long run: Repeated gambles and rationality. Br J Math Stat Psychol. 2007;60:61. pmid:17535579
- View Article
- PubMed/NCBI
- Google Scholar
50. Mogilner C, Aaker JL, Pennington GL. Time Will Tell: The Distant Appeal of Promotion and Imminent Appeal of Prevention. J Consum Res. 2008;34(5):670.
- View Article
- Google Scholar
51. Stevens JR, Stephens DW. 13: The Adaptive Nature of Impulsivity. In: Madden GJ, Bickel WK, editors. Impulsivity: The Behavioral and Neurological Science of Discounting. Washington DC: American Psychological Association; 2010. p. 361–388.
52. Pan W. Irrational Rationality: Decision Making in Aspects of Time. International Journal of Management and Applied Science. 2016;2(8):98.
- View Article
- Google Scholar
53. Mikels JA, Reed AE. Monetary losses do not loom large in later life: Age differences in the framing effect. J Gerontol. 2009;64B:457.
- View Article
- Google Scholar
54. Koop GJ, Johnson JG. The use of multiple reference points in risky decision making. J Behav Dec Making. 2012;25:49.
- View Article
- Google Scholar
55. Wulff DU, Hills TT, Hertwig R. How short- and long-run aspiration impact search and choice in decisions from experience. Cognition. 2015;144:29. pmid:26218515
- View Article
- PubMed/NCBI
- Google Scholar
56. Adler A. The Science of Living. George Allen and Unwin Ltd.; 1930.
57. Zou Q, Li J, Hong Q, Lin Z, Wu Y, Shi H, et al. Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods. Biomed Res Int. 2015; p. 810514. pmid:26273645
- View Article
- PubMed/NCBI
- Google Scholar
58. Zeng X, Zhang X, Zou Q. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Brief Bioinform. 2016;17(2):193. pmid:26059461
- View Article
- PubMed/NCBI
- Google Scholar
59. Zeng X, Liao Y, Liu Y, Zou Q. Prediction and Validation of Disease Genes Using HeteSim Scores. IEEE/ACM Trans Comput Biol Bioinform. 2017;14(3):687. pmid:26890920
- View Article
- PubMed/NCBI
- Google Scholar
60. Agnarsson G, Greenlaw R. 4. In: Graph Theory: Modeling, Application, and Algorithms. pearson international edition ed. Upper Saddle River, NJ 07458, USA: Pearson Education, Inc.; 2007. p. 104.
61. Puterman ML. Markov Decision Processes. In: Heyman DP, Sobel MJ, editors. Handbooks in Operations Research and Management Science. vol. 2. Elsevier Science Publisher B. V.; 1990. p. 331.
62. Bertsekas DP, Tsitsiklis JN. An Analysis of Stochastic Shortest Path Problems. Math Oper Res. 1991;16(3):580.
- View Article
- Google Scholar
63. Bäuerle N, Rieder U. Markov Decision Processes. Jahresber Dtsch Math Ver. 2010;112:217.
- View Article
- Google Scholar
64. Read D, Loewenstein G, Rabin M. Choice Bracketing. J Risk Uncertain. 1999;19:171.
- View Article
- Google Scholar
65. Erev I, Ert E, Plonsky O, Cohen D, Cohen O. From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience. Psychol Rev. 2017;124(4):369. pmid:28277716
- View Article
- PubMed/NCBI
- Google Scholar
66. Wulff DU, Mergenthaler-Canseco M, Hertwig R. A Meta-Analytic Review of Two Modes of Learning and the Description-Experience Gap. Psychol Bull. 2018;144(2):140. pmid:29239630
- View Article
- PubMed/NCBI
- Google Scholar
67. Payne JW. It is Whether You Win or Lose: The Importance of the Overall Probabilities of Winning or Losing in Risky Choice. J Risk Uncertain. 2005;30:5.
- View Article
- Google Scholar
68. Ert E, Erev I. On the descriptive value of loss aversion in decisions under risk: Six clarifications. Judgm Decis Mak. 2013;8(3):214.
- View Article
- Google Scholar
69. Finucane ML, Alhakami A, Slovic P, Johnson SM. The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making. 2000;13:1.
- View Article
- Google Scholar
70. Slovic P, Finucane M, Peters E, MacGregor DG. Rational actors or rational fools? Implications of the affect heuristic for behavioral economics. J Socio-Econ. 2002;31:329.
- View Article
- Google Scholar
71. Slovic P, Finucane ML, Peters E, MacGregor DG. The affect heuristic. Eur J Oper Res. 2007;177:1333.
- View Article
- Google Scholar
72. Bateman I, Dent S, Peters E, Slovic P, et al. The Affect Heuristic and the Attractiveness of Simple Gambles. J Behav Dec Making. 2007;20:365.
- View Article
- Google Scholar
73. Perc M, Szolnoki A. Social diversity and promotion of cooperation in the spatial prisoner’s dilemma game. Phys Rev E. 2008;77:011904.
- View Article
- Google Scholar
74. Chen M, Wang L, Sun S, Wang J, Xia C. Evolution of Cooperation in the spatial public goods game with adaptive reputation assortment. Phys Lett A. 2016;380:40.
- View Article
- Google Scholar
75. Wang J, Lu W, Liu L, Li L, Xia C. Utility Evaluation Based on One-to-N Mapping in the Prisoner’s Dilemma Game for Interdependent Networks. PLoS ONE. 2016;11(12):e0167083. pmid:27907024
- View Article
- PubMed/NCBI
- Google Scholar
76. Wang C, Wang L, Wang J, Sun S, Xia C. Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices. Appl Math Comput. 2017;293:18.
- View Article
- Google Scholar
77. Gale J, Binmore KG, Samuelson L. Learning to be imperfect: the ultimatum Game. Game Econ Behav. 1995;8(1):56.
- View Article
- Google Scholar

[ref1] 1. Bermúdez JL. 1. In: Decision Theory and Rationality. Oxford University Press; 2009. p. 1–5.

[ref2] 2. Alchian AA. Uncertainty, Evolution, And Economic Theory. J Polit Econ. 1950;58(3):211.
View Article
Google Scholar

[3] View Article

[4] Google Scholar

[ref3] 3. Frank RH. If Homo Economicus Could Choose His Own Utility Function, Would He Want One with a Conscience? Am Econ Rev. 1987;77(4):593.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref4] 4. Opaluch JJ, Segerson K. Rational Roots of “Irrational” Behavior: New Theories of Economic Decision-Making. Northeastern Journal of Agricultural and Resource Economics. 1989;18(2):81.
View Article
Google Scholar

[9] View Article

[10] Google Scholar

[ref5] 5. Bechara A, Damasio H, Tranel D, Damasio AR. Deciding Advantageously Before Knowing the Advantageous Strategy. Science. 1997;275 (5304). pmid:9036851
View Article
PubMed/NCBI
Google Scholar

[12] View Article

[13] PubMed/NCBI

[14] Google Scholar

[ref6] 6. McFadden D. Rationality for Economists? J Risk Uncertainty. 1999;19(1-3):73.
View Article
Google Scholar

[16] View Article

[17] Google Scholar

[ref7] 7. Slovic P, Finucane ML, Peters E, MacGregor DG. Risk as Analysis and Risk as Feelings: Some Thoughts about affect, reason, risk, and rationality. Risk Anal. 2004;24(2):311. pmid:15078302
View Article
PubMed/NCBI
Google Scholar

[19] View Article

[20] PubMed/NCBI

[21] Google Scholar

[ref8] 8. Lee D. Neural basis of quasi-rational decision making. Curr Opin Neurobiol. 2006;16:191. pmid:16531040
View Article
PubMed/NCBI
Google Scholar

[23] View Article

[24] PubMed/NCBI

[25] Google Scholar

[ref9] 9. d’Acremont M, Bossaerts P. Neurobiological studies of risk assessment: A comparison of expected utility and mean–variance approaches. Cogn Affect Behav Neurosci. 2008;8:363. pmid:19033235
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref10] 10. Roiser JP, de Martino B, Tan GCY, Kumaran D, Seymour B, Wood NW, et al. A Genetically Mediated Bias in Decision Making Driven by Failure of Amygdala Control. J Neurosci. 2009;29(18):5985. pmid:19420264
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref11] 11. von Neumann J, Morgenstern O. 1. In: Theory of Games and Economic Behavior. 3rd ed. Princeton University Press; 1980. p. 1–45.

[ref12] 12. Hey JD, Orme C. Investigating Generalizations of Expected Utility Theory Using Experimental Data. Econometrica. 1994;62(6):1291–1326.
View Article
Google Scholar

[36] View Article

[37] Google Scholar

[ref13] 13. Rostain T. Educating Homo Economicus: Cautionary Notes on the New Behavioral Law and Economics Movement. Law & Soc’y Rev. 2000;34(4).
View Article
Google Scholar

[39] View Article

[40] Google Scholar

[ref14] 14. Hooker C. Rationality as effective organisation of interaction and its naturalist framework. Axiomathes. 2011;21:99.
View Article
Google Scholar

[42] View Article

[43] Google Scholar

[ref15] 15. Allais M. Le Comportment de l’homme Rationale Devant le Rsique, Critiques des Postulates et Axiomes de l’ecole Americaine. Econometrica. 1953;21:503.
View Article
Google Scholar

[45] View Article

[46] Google Scholar

[ref16] 16. Conlisk J. Three variants of the Allais example. Amer Econ Rev. 1989;79:392.
View Article
Google Scholar

[48] View Article

[49] Google Scholar

[ref17] 17. Denes-Raj V, Epstein S. Conflict between intuitive and rational processing: When people behave against their better judgment. J Pers Soc Psychol. 1994;66:819.
View Article
Google Scholar

[51] View Article

[52] Google Scholar

[ref18] 18. Wiseman RM, Gomez-Mejia LR. A behavioral agency model of managerial risk taking. Acad Manage Rev. 1998;23:133.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref19] 19. Mongin P. Does optimization imply rationality? Synthese. 2000;124:73.
View Article
Google Scholar

[57] View Article

[58] Google Scholar

[ref20] 20. Abdellaoui M, L’Haridon O, Paraschiv C. Experienced vs. Described Uncertainty: Do We Need Two Prospect Theory Specifications? Manag Sci. 2011;57(10):1879.
View Article
Google Scholar

[60] View Article

[61] Google Scholar

[ref21] 21. Nagengast AJ, Braun DA, Wolpert DM. Risk-sensitivity and the mean-variance trade-off: decision making in sensorimotor control. Proc R Soc B. 2011;278(1716):2325. pmid:21208966
View Article
PubMed/NCBI
Google Scholar

[63] View Article

[64] PubMed/NCBI

[65] Google Scholar

[ref22] 22. Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk. Econometrica. 1979;47:263.
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref23] 23. Kahneman D, Knetsch JL, Thaler R. Fairness as a Constraint on Profit Seeking: Entitlements in the Market. Am Econ Rev. 1986;76(4):728.
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref24] 24. Tversky A, Kahneman D. Loss aversion in riskless choice: A reference-dependent model. Q J Econ. 1991;106:1039.
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref25] 25. Tversky A, Kahneman D. Advances in prospect theory: Cumulative represtation of uncertainty. J Risk Uncertain. 1992;5:297.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref26] 26. Kahneman D, Tversky A. Choices, Values, and Frames. Cambridge University Press; 2000.

[ref27] 27. Kahneman D, Krueger AB, Schkade D, Schwarz N, Stone AA. Would you be happier if you were richer? A focusing illusion. Science. 2006;312:1908. pmid:16809528
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref28] 28. Thaler RH, Johnson EJ. Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice. Manag Sci. 1990;36:643.
View Article
Google Scholar

[84] View Article

[85] Google Scholar

[ref29] 29. Martino BD, Kumaran D, Seymour B, Dolan RJ. Frames, biases, and rational decision-making in the human brain. Science. 2006;313:684. pmid:16888142
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref30] 30. Birnbaum MH. New Paradoxes of Risky Decision Making. Psychol Rev. 2008;115(2):463.
View Article
Google Scholar

[91] View Article

[92] Google Scholar

[ref31] 31. Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science. 1981;211:453. pmid:7455683
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref32] 32. Druckman JN, McDermott R. Emotion and the framing of risky choice. Polit Behav. 2008;30:297.
View Article
Google Scholar

[98] View Article

[99] Google Scholar

[ref33] 33. Gächter S, Orzen H, Renner E, Starmer C. Are experimental economists prone to framing effects? A natural field experiment. J Econ Behav Organ. 2009;70:443.
View Article
Google Scholar

[101] View Article

[102] Google Scholar

[ref34] 34. Good IJ. Rational Decisions. J R Statist Soc B. 1952;14(1):107.
View Article
Google Scholar

[104] View Article

[105] Google Scholar

[ref35] 35. Davidson P. Rational expectations: a fallacious foundation for studying crucial decision-making processes. J Post Keynesian Econ. 1982-83;5(2):182.
View Article
Google Scholar

[107] View Article

[108] Google Scholar

[ref36] 36. Bausor R. The Limits of Rationality. Social Concept. 1985;2:66.
View Article
Google Scholar

[110] View Article

[111] Google Scholar

[ref37] 37. Anand P. The Nature of Rational Choice and The Foundation of Statistics. Oxford Economic Papers, New Series. 1991;43(2):199.
View Article
Google Scholar

[113] View Article

[114] Google Scholar

[ref38] 38. Bourgeois-Gironde S, Giraud R. Framing effects as violations of extensionality. Theory Dec. 2009;67:385.
View Article
Google Scholar

[116] View Article

[117] Google Scholar

[ref39] 39. Kirman A, Livet P, Teschl M. Rationality and emotions. Phil Trans R Soc B. 2010;365:219.
View Article
Google Scholar

[119] View Article

[120] Google Scholar

[ref40] 40. Dawkins R. 11. In: The Selfish Gene. 30th ed. Oxford University Press; 2006. p. 189–201.

[ref41] 41. Samuelson PA. Risk and Uncertainty: A Fallacy of Large Numbers. Scie. 1963;98:108.
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref42] 42. Tversky A, Bar-Hillel M. Risk: The Long and the Short. J Exp Psychol Learn Mem Cogn. 1983;9(4):713.
View Article
Google Scholar

[126] View Article

[127] Google Scholar

[ref43] 43. Redelmeier DA, Tversky A. Discrepancy between medical decisions for individual patients and for groups. N Engl J Med. 1990;322(16):1162. pmid:2320089
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref44] 44. Benartzi S, Thaler RH. Risk aversion or myopia? Choices in repeated gambles and retirement investments. Manag Sci. 1999;45(3):364.
View Article
Google Scholar

[133] View Article

[134] Google Scholar

[ref45] 45. Mallpress DEW, Fawcett TW, Houston AI, McNamara JM. Risk Attitudes in a Chaging Environment: An Evolutionary Model of the Fourfold Pattern of Risk Preferences. Psychol Rev. 2015;122(2):364. pmid:25844877
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref46] 46. Lopes LL. Notes, Comments, and New Findings Decision Making in the Short Run. J Exp Psychol Hum Learn. 1981;7(5):377.
View Article
Google Scholar

[140] View Article

[141] Google Scholar

[ref47] 47. Lopes LL. When Time is of the Essence: Averaging, Aspiration, and the Short Run. Organ Behav Hum Decis Process. 1996;65(3):179.
View Article
Google Scholar

[143] View Article

[144] Google Scholar

[ref48] 48. Fried Y, Slowik LH. Enriching Goal-seeting theory with time: an integrated approach. Acad Manage Rev. 2004;29(3):404.
View Article
Google Scholar

[146] View Article

[147] Google Scholar

[ref49] 49. Aloysius JA. Decision making in the short and long run: Repeated gambles and rationality. Br J Math Stat Psychol. 2007;60:61. pmid:17535579
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref50] 50. Mogilner C, Aaker JL, Pennington GL. Time Will Tell: The Distant Appeal of Promotion and Imminent Appeal of Prevention. J Consum Res. 2008;34(5):670.
View Article
Google Scholar

[153] View Article

[154] Google Scholar

[ref51] 51. Stevens JR, Stephens DW. 13: The Adaptive Nature of Impulsivity. In: Madden GJ, Bickel WK, editors. Impulsivity: The Behavioral and Neurological Science of Discounting. Washington DC: American Psychological Association; 2010. p. 361–388.

[ref52] 52. Pan W. Irrational Rationality: Decision Making in Aspects of Time. International Journal of Management and Applied Science. 2016;2(8):98.
View Article
Google Scholar

[157] View Article

[158] Google Scholar

[ref53] 53. Mikels JA, Reed AE. Monetary losses do not loom large in later life: Age differences in the framing effect. J Gerontol. 2009;64B:457.
View Article
Google Scholar

[160] View Article

[161] Google Scholar

[ref54] 54. Koop GJ, Johnson JG. The use of multiple reference points in risky decision making. J Behav Dec Making. 2012;25:49.
View Article
Google Scholar

[163] View Article

[164] Google Scholar

[ref55] 55. Wulff DU, Hills TT, Hertwig R. How short- and long-run aspiration impact search and choice in decisions from experience. Cognition. 2015;144:29. pmid:26218515
View Article
PubMed/NCBI
Google Scholar

[166] View Article

[167] PubMed/NCBI

[168] Google Scholar

[ref56] 56. Adler A. The Science of Living. George Allen and Unwin Ltd.; 1930.

[ref57] 57. Zou Q, Li J, Hong Q, Lin Z, Wu Y, Shi H, et al. Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods. Biomed Res Int. 2015; p. 810514. pmid:26273645
View Article
PubMed/NCBI
Google Scholar

[171] View Article

[172] PubMed/NCBI

[173] Google Scholar

[ref58] 58. Zeng X, Zhang X, Zou Q. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Brief Bioinform. 2016;17(2):193. pmid:26059461
View Article
PubMed/NCBI
Google Scholar

[175] View Article

[176] PubMed/NCBI

[177] Google Scholar

[ref59] 59. Zeng X, Liao Y, Liu Y, Zou Q. Prediction and Validation of Disease Genes Using HeteSim Scores. IEEE/ACM Trans Comput Biol Bioinform. 2017;14(3):687. pmid:26890920
View Article
PubMed/NCBI
Google Scholar

[179] View Article

[180] PubMed/NCBI

[181] Google Scholar

[ref60] 60. Agnarsson G, Greenlaw R. 4. In: Graph Theory: Modeling, Application, and Algorithms. pearson international edition ed. Upper Saddle River, NJ 07458, USA: Pearson Education, Inc.; 2007. p. 104.

[ref61] 61. Puterman ML. Markov Decision Processes. In: Heyman DP, Sobel MJ, editors. Handbooks in Operations Research and Management Science. vol. 2. Elsevier Science Publisher B. V.; 1990. p. 331.

[ref62] 62. Bertsekas DP, Tsitsiklis JN. An Analysis of Stochastic Shortest Path Problems. Math Oper Res. 1991;16(3):580.
View Article
Google Scholar

[185] View Article

[186] Google Scholar

[ref63] 63. Bäuerle N, Rieder U. Markov Decision Processes. Jahresber Dtsch Math Ver. 2010;112:217.
View Article
Google Scholar

[188] View Article

[189] Google Scholar

[ref64] 64. Read D, Loewenstein G, Rabin M. Choice Bracketing. J Risk Uncertain. 1999;19:171.
View Article
Google Scholar

[191] View Article

[192] Google Scholar

[ref65] 65. Erev I, Ert E, Plonsky O, Cohen D, Cohen O. From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience. Psychol Rev. 2017;124(4):369. pmid:28277716
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref66] 66. Wulff DU, Mergenthaler-Canseco M, Hertwig R. A Meta-Analytic Review of Two Modes of Learning and the Description-Experience Gap. Psychol Bull. 2018;144(2):140. pmid:29239630
View Article
PubMed/NCBI
Google Scholar

[198] View Article

[199] PubMed/NCBI

[200] Google Scholar

[ref67] 67. Payne JW. It is Whether You Win or Lose: The Importance of the Overall Probabilities of Winning or Losing in Risky Choice. J Risk Uncertain. 2005;30:5.
View Article
Google Scholar

[202] View Article

[203] Google Scholar

[ref68] 68. Ert E, Erev I. On the descriptive value of loss aversion in decisions under risk: Six clarifications. Judgm Decis Mak. 2013;8(3):214.
View Article
Google Scholar

[205] View Article

[206] Google Scholar

[ref69] 69. Finucane ML, Alhakami A, Slovic P, Johnson SM. The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making. 2000;13:1.
View Article
Google Scholar

[208] View Article

[209] Google Scholar

[ref70] 70. Slovic P, Finucane M, Peters E, MacGregor DG. Rational actors or rational fools? Implications of the affect heuristic for behavioral economics. J Socio-Econ. 2002;31:329.
View Article
Google Scholar

[211] View Article

[212] Google Scholar

[ref71] 71. Slovic P, Finucane ML, Peters E, MacGregor DG. The affect heuristic. Eur J Oper Res. 2007;177:1333.
View Article
Google Scholar

[214] View Article

[215] Google Scholar

[ref72] 72. Bateman I, Dent S, Peters E, Slovic P, et al. The Affect Heuristic and the Attractiveness of Simple Gambles. J Behav Dec Making. 2007;20:365.
View Article
Google Scholar

[217] View Article

[218] Google Scholar

[ref73] 73. Perc M, Szolnoki A. Social diversity and promotion of cooperation in the spatial prisoner’s dilemma game. Phys Rev E. 2008;77:011904.
View Article
Google Scholar

[220] View Article

[221] Google Scholar

[ref74] 74. Chen M, Wang L, Sun S, Wang J, Xia C. Evolution of Cooperation in the spatial public goods game with adaptive reputation assortment. Phys Lett A. 2016;380:40.
View Article
Google Scholar

[223] View Article

[224] Google Scholar

[ref75] 75. Wang J, Lu W, Liu L, Li L, Xia C. Utility Evaluation Based on One-to-N Mapping in the Prisoner’s Dilemma Game for Interdependent Networks. PLoS ONE. 2016;11(12):e0167083. pmid:27907024
View Article
PubMed/NCBI
Google Scholar

[226] View Article

[227] PubMed/NCBI

[228] Google Scholar

[ref76] 76. Wang C, Wang L, Wang J, Sun S, Xia C. Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices. Appl Math Comput. 2017;293:18.
View Article
Google Scholar

[230] View Article

[231] Google Scholar

[ref77] 77. Gale J, Binmore KG, Samuelson L. Learning to be imperfect: the ultimatum Game. Game Econ Behav. 1995;8(1):56.
View Article
Google Scholar

[233] View Article

[234] Google Scholar

Figures

Abstract

Introduction

Methods

Game set: Construction of the network

Strategy matrix, W

Matrices

Iteration: State vector and iteration relation

Strategy comparison

Results

Fixed option strategy

Baseline for comparison: The min k strategy

The riskiest decision maker: The max k strategy

Comparison of the strategies

Discussions

Expected utility revisited

Measurement of goal in mind

Prospect theory revisited

Mathematical interpretation of economical behaviors

Perspectives

Conclusion

Acknowledgments

References