Narrow Results

One of the missions of the cognitive process of animals, including humans, is to make reasonable judgments and decisions in the presence of uncertainty. The balance between exploration and exploitation investigated in the reinforcement-learning paradigm is one of the key factors in this process. Recently, following the pioneering work in behavioral economics, growing attention has been directed to human behaviors exhibiting deviations from the simple maximization of external reward. Here we study the dynamics of betting behavior in a simple game, where the probability of reward and the magnitude of reward are designed to give a "zero" expected net reward ("flat reward condition"). No matter how the subject behaves, there is on average no change in one's resources, and therefore every possible sequence of action has the same value. Even in such a situation, the subjects are found to behave not in a random manner, but in ways showing characteristic tendencies, reflecting the dynamics of brain's reward system. Our results suggest that brain's reward system is characterized by a rich and complex dynamics only loosely coupled with external reward structure.

Incentive institutions that reward cooperators and punish free-riders are often used to promote cooperation in public goods games. We show that for incentives of intermediate size, a sanctioning institution that punishes the worst players can sustain full cooperation and that a rewarding institution can promote cooperation only if lower contributors also have the chance to win the reward. Furthermore, if the incentive institution can provide both reward and punishment, then it should use reward as much as possible. The group welfare is maximized when the punishment is just barely larger than the minimum required to obtain the full contribution.

In this paper, we present a model to describe the evolution of the yield spread by considering the rating evaluation as the determinant of credit spreads. The underlying rating migration process is assumed to be a non-homogeneous discrete time semi-Markov process. We calculate the total sum of mean basis points paid within any given time interval. From this information we show how it is possible to extract the time evolution of expected interest rates and discount factors.

Humanity has been fascinated by the pursuit of fortune since time immemorial, and many successful outcomes benefit from strokes of luck. But success is subject to complexity, uncertainty, and change — and at times becoming increasingly unequally distributed. This leads to tension and confusion over to what extent people actually get what they deserve (i.e. fairness/meritocracy). Moreover, in many fields, humans are overconfident and pervasively confuse luck for skill (I win, it is skill; I lose, it is bad luck). In some fields, there is too much risk-taking; in others, not enough. Where success derives in large part from luck — and especially where bailouts skew the incentives (heads, I win; tails, you lose) — it follows that luck is rewarded too much. This incentivizes a culture of gambling, while downplaying the importance of productive effort. And, short-term success is often rewarded, irrespective, and potentially at the detriment, of the long-term system fitness. However, much success is truly meritocratic, and the problem is to discern and reward based on merit. We call this the fair reward problem. To address this, we propose three different measures to assess merit: (i) raw outcome; (ii) risk-adjusted outcome, and (iii) prospective. We emphasize the need, in many cases, for the deductive prospective approach, which considers the potential of a system to adapt and mutate in novel futures. This is formalized within an evolutionary system, comprised of five processes, inter alia handling the exploration–exploitation trade-off. Several human endeavors — including finance, politics, and science — are analyzed through these lenses, and concrete solutions are proposed to support a prosperous and meritocratic society.

Punishment and reward are usually regarded as two potential mechanisms to explain the evolution of cooperation especially among multiple participators. However, the performance of these two scenarios in spatial environment needs to be discussed. To figure out this issue, we resort to the $n$-player Iterated Snowdrift Dilemma (ISD) game and Iterated Prisoner’s Dilemma (IPD) game. More importantly, the evolution of punishment and reward in social network-structured populations has not been formally addressed. The numerical results show the equilibrium cooperation frequency can be influenced by cost-to-benefit ratio $r$, the punishment-to-benefit ratio $p∕b$ and the reward-to-benefit ratio $r∕b$. And one intriguing observation is that under the same situation, the punishment is more effective than reward to the population. Then we further probe the effectiveness of neighborhood relationship to the cooperation, which is reflected by the random rewired probability $p_r$. From the distribution of the four roles of the participator we can find that individuals can cooperate easily when they have close relationship. The results of this paper may be helpful to understand the cooperation in complex project or among industry–university–research cooperation project.

This paper introduces a method to solve the 2D irregular packing problem using Deep Reinforcement Learning (Deep RL) for logistics. Our method employs a Q agent trained to predict the best placement within a container, maximizing available space. Unlike previous Deep RL algorithms, our method introduces a dense reward function at each packing step, providing immediate feedback and accelerating learning. To our knowledge, this is the first approach to use a dense reward to address the 2D irregular packing problem. Building on our earlier work, we improve the deep neural network by incorporating the Double Deep Q-Network (DDQN) framework to enhance our deep Q-learning approach, reducing overestimation biases and improving decision-making reliability. Simulation results show the method’s effectiveness in completing the online 2D irregular packing tasks, achieving promising volume efficiency and packed piece metrics. This research extends our initial findings, highlighting the practical importance of DDQN and dense reward in advancing 2D irregular packing problem-solving. These advancements not only broaden the applications of deep learning but also hold practical importance for real-world logistics challenges.

We consider a risk averse investor as are who has a bond, is rewarded with $\alpha$ units of company stock option and is endowed with stochastic income. First, we derive and analyzes the optimal investment threshold and investment value function of the investor. Second, we determine a remuneration package and the utility indifference pricing of this risk averse investor and analyze the results for two utility functions; the exponential and logarithmic utilities. From these results, we conclude that the more units of company stock the investor receives, the higher the chances of increasing the total wealth or profit.

Colors are critical for understanding the emotional aspect of the human artistic mind, such as that found in painting a landscape, still life, or portrait. First, we report how single colors are memorized in the brain; second, how pairs of colors harmonize in the dissociated brain under the influence of the emotional brain; third, we see how colored paintings are appreciated as beautiful or ugly in the dissociated brain areas led by the intrinsic reward system in the human brain. The orbitofrontal brain is probably one of the vital brain areas that brings us a value-based reward system that makes a unique contribution to emotional neuroaesthetics.