Determination of Break-Even Point through Consumer Preference Relation

Social choice theory beliefs about how the consumers function to chose their interested goods and services. Preference relation with affine indifference curves that has a concave representation has a linear utility representation. This study asks how individual preference relations might be combined to give a single ordering which captures the overall wishes of the group of individuals. There are certain properties that one would like such a utility rule, utility have thus become a more abstract concept that is not necessarily solely based on the satisfaction or pleasure received. Concept of cardinal utility is studied in three different situations Debreu (1958) gave quite different approach. This study maintains link between mathematical theory and financial concept to determine break-even point through the consumers’ preference relation.

Ergodicity-breaking reveals time optimal decision making in humans

Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how individuals should tolerate risk in different environments. To optimise wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing decision theories. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximise the time average growth of in-game wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.

Learning in Markets: Greed Leads to Chaos but Following the Price is Right

We study learning dynamics in distributed production economies such as blockchain mining, peer-to-peer file sharing and crowdsourcing. These economies can be modelled as multi-product Cournot competitions or all-pay auctions (Tullock contests) when individual firms have market power, or as Fisher markets with quasi-linear utilities when every firm has negligible influence on market outcomes. In the former case, we provide a formal proof that Gradient Ascent (GA) can be Li-Yorke chaotic for a step size as small as Θ(1/n), where n is the number of firms. In stark contrast, for the Fisher market case, we derive a Proportional Response (PR) protocol that converges to market equilibrium. The positive results on the convergence of the PR dynamics are obtained in full generality, in the sense that they hold for Fisher markets with any quasi-linear utility functions. Conversely, the chaos results for the GA dynamics are established even in the simplest possible setting of two firms and one good, and they hold for a wide range of price functions with different demand elasticities. Our findings suggest that by considering multi-agent interactions from a market rather than a game-theoretic perspective, we can formally derive natural learning protocols which are stable and converge to effective outcomes rather than being chaotic.

Improved incentive pricing-based quasi-linear utility function of wireless networks

The model of the incentive pricing scheme-based quasi-linear utility function in wireless network was designed. Previous research seldom focusses on user’s satisfaction while using network. Therefore, the model is then attempted to be set up that is derived from the modification of bundling and models of reverse charging and maintain the quality of service to users by utilizing quasi-linear utility function. The pricing schemes then are applied to local data server traffic. The model used is known as mathematical programming problem that can be solved by LINGO 13.0 program as optimization tool to get the optimal solution. The optimal results show that the improved incentive pricing can achieve better solution compared to original reverse charging where the models will be obtained in flat fee, usage-based, and two-part tariff strategies for homogeneous consumers.

A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information

Discrete choice modeling of travel modes is an essential part of traffic planning and management. Thus far, this field has been dominated by multinomial logit (MNL) models with a linear utility specification. However, deep neural networks (DNNs), owing to their powerful capacity of nonlinear fitting, are now rapidly replacing these models. This is because, by using DNNs, mode choice can be assimilated with the classification problems within the machine learning community. This article proposes a newly designed DNN framework for traffic mode choice in the style of two hidden layers. First, a local-connected layer automatically extracts an effective utility specification from the available data, and then, a fully connected layer augments the feature representation. Validated by a practical city-wide multimodal traffic dataset in Beijing, our model significantly outperforms the random utility models and simple fully connected neural network in terms of the prediction accuracy. Besides the comparison of the predictive power, we also present the interpretability of the proposed model.

Competition Among Contests: a Safety Level Analysis

We study a competition among two contests, where each contest designer aims to attract as much effort as possible. Such a competition exists in reality, e.g., in crowd-sourcing websites. Our results are phrased in terms of the ``relative prize power'' of a contest, which is the ratio of the total prize offered by this contest designer relative to the sum of total prizes of the two contests. When contestants have a quasi-linear utility function that captures both a risk-aversion effect and a cost of effort, we show that a simple contest attracts a total effort which approaches the relative prize power of the contest designer assuming a large number of contestants. This holds regardless of the contest policy of the opponent, hence providing a ``safety level'' which is a robust notion similar in spirit to the max-min solution concept.

Fishing location choice and risk preferences among small fishers – Implications for fisheries management policies

The study provides evidence for how risk preferences determine fishing location choices by artisanal fishers on the south-west coast of the island of Mauritius. Risk preference is modelled using a random linear utility framework defined over mean-standard deviation space. The study estimates expected revenue and revenue risk from the Just and Pope production function and applies the random parameter logit model to account for fisher-specific and location-specific characteristics. The findings are consistent with utility-maximising fishers, whereby the likelihood to choose a fishing location is positively associated with expected revenue and negatively related to revenue risk. Distance from fishing station to fishing grounds affects the choice of fishing location negatively. The estimated model allows heterogeneity in risk preferences and concludes that 51% of fishers can be classified as risk averse, 31% as risk seekers and the remaining as risk neutral. The study also estimates the degree of substitutability and complementarity between fishing locations based on the risk preferences of fishers and discusses the relevance of this for fisheries management policy.