General Guidelines for the Use of Colour on Electronic Charts

An Electronic Chart Testbed has been developed by the Canadian Hydrographic Service for the purposes of investigating design and safety aspects of using electronic charts as a navigational aid for mariners. The proper selection and specification of colour is a fundamental aspect of effective display design. This report outlines the issues involved in the use of colour on displays as they relate to the Electronic Chart Display and Information System (ECDIS). Topics include luminance, high and low ambient illumination, brigthness, display background, colour selection, information clutter, colour coding convention, stimulus size, image location, visual effects, and user characteristics. Since ECDIS is relatively young in its development, the purpose of the review is to provide some general guidelines for selecting and using colours on electronic charts

Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices

Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moir\ue9 pattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theory-guided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including in situ and ex situ methods; and properties and applications that are related to moir\ue9 engineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field

Predicting Volatile Consumer Markets using Multi-agent Methods: Theory and Validation

A behavioral model incorporating utility based rational choice enhanced with psychological drivers is presented to study a consumer goods market, characterized by repeat purchase incidences by households. The psychological drivers incorporate purchase strategies of loyalty and change-of-pace, which affect the choice set of consumer agents in an agent based simulation environment. Agent specific memories of past purchases drive these strategies, while attribute specific preferences and prices drive the utility based choice function. Transactions data from a category in a supermarket is used to initialize, calibrate and test the accuracy of predictions of the model. Results indicate that prediction accuracy at both macro and micro levels can be significantly improved with the incorporation of purchase strategies. Moreover, increasing the memory length beyond a certain limit does not improve predictions in the model, indicating that consumer memory of past shopping instances is finite and low and recent purchase history is more relevant to current decision making than the distant past

VOLATILITY IN THE CONSUMER PACKAGED GOODS INDUSTRY — A SIMULATION BASED STUDY

The volatility in a CPG market is modeled using a bottom-up simulation approach and validated against disaggregated supermarket transactions data. The simulation uses independent agents, each agent representing unique households in the data. A simple behavioral model incorporates household preferences for product attributes and prices. Our validation strategy tests the model predictions at both macro and micro levels and benchmarks the performance in each against a random choice model. The model significantly outperforms the benchmark at both levels. At the macro level, choices made by heterogenous agents accurately captures the volatility in market shares over time. This accuracy at the macro level is driven by the accuracy of predictions at the micro household level SKU and attribute choice

Predicting Volatile Consumer Markets using Multi-Agent Methods

A behavioral model incorporating utility-based rational choice enhanced with psychological drivers is presented to study a consumer goods market, characterized by repeat purchase incidences by households. The psychological drivers incorporate purchase strategies of loyalty and change-of-pace, which affect the choice set of consumer agents in an agent-based simulation environment. Agent specific memories of past purchases drive these strategies, while attribute specific preferences and prices drive the utility-based choice function. Transactions data from a category in a supermarket is used to initialize, calibrate, and test the accuracy of predictions of the model. Results indicate that prediction accuracy at both macro and micro levels can be significantly improved with the incorporation of purchase strategies. Moreover, increasing the memory length beyond a certain limit does not improve predictions in the model, indicating that consumer memory of past shopping instances is finite and low and recent purchase history is more relevant to current decision making than the distant past.</jats:p

Modelling of Consumer Goods Markets: An Agent Based Computational Approach

An agent based behavioral model incorporating utility based rational choice enhanced with psychological drivers is presented to study a typical consumer market. The psychological drivers incorporate purchase strategies of loyalty and change-of-pace, using agent specific memory of past purchases. Attribute specific preferences and prices drive the utility based choice function. Transactions data is used to calibrate and test the model. Results indicate that prediction accuracy at both macro and micro levels can be significantly improved with the incorporation of purchase strategies. Moreover, increased agent memory does not improve predictions in the model beyond a threshold, indicating that consumer memory of past shopping instances is finite and recent purchase history is more relevant to current decision making than the distant past. The article illustrates the use of agent based simulations to model changes or interventions in the market, such as new product introductions, for which no past history exists

VOLATILITY IN THE CONSUMER PACKAGED GOODS INDUSTRY — A SIMULATION BASED STUDY

The volatility in a CPG market is modeled using a bottom-up simulation approach and validated against disaggregated supermarket transactions data. The simulation uses independent agents, each agent representing unique households in the data. A simple behavioral model incorporates household preferences for product attributes and prices. Our validation strategy tests the model predictions at both macro and micro levels and benchmarks the performance in each against a random choice model. The model significantly outperforms the benchmark at both levels. At the macro level, choices made by heterogenous agents accurately captures the volatility in market shares over time. This accuracy at the macro level is driven by the accuracy of predictions at the micro household level SKU and attribute choice.Complex system, validation, market dynamics, social simulation