Abstract: A method for an electric vehicle trip simulator is described. The method includes determining parameters for an upcoming trip of an electric vehicle, including at least a start location and a destination location. The method also includes analyzing data associated with a plurality of points of interest between the start location and the destination location. The method further includes selecting a set of locations from the plurality of points of interest for the upcoming trip of the electric vehicle, according to the analyzing of the data. The method also includes displaying a travel order recommendation associated with the set of locations selected for the upcoming trip of the electric vehicle, including at least one charging station location according to a current charge capacity of the electric vehicle.

Abstract: Systems and methods for determining whether a user employs System 1 type thinking or System 2 type thinking when engaged in a task are disclosed. The systems and methods include determining one or more properties of the task based on information regarding the task received from a database storing information regarding the task, determining one or more properties of the user with respect to the task, determining a state of the user based on one or more physiological sensors configured to sense one or more characteristics of the user, and determining that the user employs System 1 type thinking or System 2 type thinking when engaged in the task based on the determined one or more properties of the task, the determined one or more properties of the user, and the determined state of the user.

Abstract: Systems and methods for generating predicted preferences are disclosed. The method includes implementing, with a computing device having a processor and a non-transitory computer-readable memory, a conjoint architecture comprising: an autoencoder trained to transform input data including one or more choices and one or more features into a latent representation, and a choice classification network trained to predict one or more predicted preferences from the latent representation extracted by the autoencoder. The method further includes outputting, from the choice classification network, the one or more predicted preferences.

Abstract: A method of identifying causal relationships includes receiving data comprising a set of values corresponding to one or more variables, and generating a list of candidate causal models of relationships between or within the variables. The list is ranked based on a likelihood of each candidate causal model, wherein the likelihood includes at least a correlation value. The method further includes receiving feedback identifying a candidate causal model and a change in rank of the candidate causal model, re-ranking the list based on the feedback, and displaying the re-ranked list. The method generates an intervention comprising a suggested modification corresponding to a variable of a selected causal model among the candidate causal models in the re-ranked list, receives additional data corresponding to the variable of the suggested modification and evaluates the additional data to determine whether the likelihood of the selected causal model has changed.

Abstract: A method for use of elicited factors to inform intervention suggestions is described. The method includes identifying a demographic of a user according to a background of the user. The method also includes predicting factors including an associated set of barriers and/or motivators regarding a preference according to the demographic of the user. The method further includes predicting a plurality of interventions to shift the preference of the user according to the associated set of barriers and/or motivators. The method also includes presenting a selected one of the plurality of determined interventions to the user.

Abstract: System, methods, and other embodiments described herein relate to a manner of determining an interpretable model from experimental data using tokenization in a prediction model. In one embodiment, a method includes outputting a bit pattern of probable tokens generated from raw data using a model. The method also includes converting, using the model, the bit pattern into output tokens and parsing the output tokens into a symbolic expression. The method also includes fitting symbolic parameters from the symbolic expression into an interpretable model for accuracy. The method also includes estimating an operational behavior and signal output of a vehicle system according to the interpretable model.

Abstract: A method for calculating generalized utilities and choice predictions is described. The method includes identifying an individual's choice a user desires to predict and relevant parameters influencing the individual's choice. The method also includes manually selecting between different function forms and parameter estimates for an expected generalized utility (EGU) model if a choice data is unavailable. The method further includes providing a machine learning (ML)-based recommendation for the function forms and parameter estimates if the choice data is available. The method also includes displaying a predicted choice as well as a confidence interval associated with the predicted choice estimated using the EGU model.

Abstract: A method for graphical information de-biasing is described. The method includes detecting biased graphical information presented to a user. The method also includes generating a neutral version of the biased graphical information to form de-biased graphical information. The method further includes displaying the de-biased graphical information to the user, including highlighted components of the de-biased graphical information.

Abstract: A method for graphical information de-biasing is described. The method includes detecting biased graphical information presented to a user. The method also includes generating a neutral version of the biased graphical information to form de-biased graphical information. The method further includes displaying the de-biased graphical information to the user, including highlighted components of the de-biased graphical information.

Abstract: In accordance with one embodiment of the present disclosure, a system includes a processor, a memory communicatively coupled to the processor, and machine-readable instructions stored in the memory. The machine-readable instructions, when executed by the processor, cause the processor to perform operations including receiving a multimedia file having a metadata and a text data, the multimedia file and the text data corresponding to a user. Operations also include determining a reliability status of the multimedia file based on the multimedia file, the text data, or combinations thereof. Operations further include determining a bias status of the user based on the multimedia file, the text data, or combinations thereof, and generating a report comprising the reliability status and the bias status of the multimedia file.

Abstract: A method for monitoring user decision making activity is described. The method includes logging a user decision and decision communications corresponding to the user decision. The method also includes identifying the user decision as a compromised user decision based on an emotional status of a user determined from the decision communications. The method further includes determining a subsequent emotional status of the user based on a subsequent user communication corresponding to the compromised user decision. The method also includes providing an advice recommendation to the user when a degraded emotional status is detected regarding the compromised decision.

Abstract: A method comprises identifying a representation of first statistical information in a document, identifying descriptive text that describes the first statistical information, determining whether the descriptive text accurately describes the first statistical information, and upon determination that the descriptive text does not accurately describe the first statistical information, generating alternative descriptive text that accurately describes the first statistical information.

Abstract: A system and method for configuring a device includes using a machine learning model to generate a user behavior model based on user behavior data. The user behavior data may include time series data collected from user interactions with a first device, and the machine learning model may include a classification model configured to classify the user behavior data into the one or more classifications. A mapping may be created by training a machine learning model, using user behavior models from a plurality of users and device settings from the plurality of users, to identify one or more relationships between device settings and classifications of the user behavior data. The system and method configures one or more settings of a second device based on the user behavior model and the mapping.

Abstract: A method includes fitting a ML trained model to data features, the fitting generates complete data feature-set outputs that are associated with a first set of accuracy values, iteratively fitting, after an iterative removal of each data feature from the data feature-set, the ML trained model to subsets of the plurality of data features to determine respective reduced feature-set outputs, each subset lacks a different data feature of the plurality of data features, determining one or more of the reduced feature-set outputs as corresponding to a second set of accuracy values, designating the iteratively removed data features as accuracy-modifying data features, generating a first linear model, generating a second linear model based on one of the accuracy-modifying data features having a weight that is highest relative to respective different weights of the remaining ones of the accuracy-modifying data features, and identifying the second linear model as a generative model.

Abstract: A method of identifying causal relationships includes receiving data comprising a set of values corresponding to one or more variables, and generating a list of candidate causal models of relationships between or within the variables. The list is ranked based on a likelihood of each candidate causal model, wherein the likelihood includes at least a correlation value. The method further includes receiving feedback identifying a candidate causal model and a change in rank of the candidate causal model, re-ranking the list based on the feedback, and displaying the re-ranked list. The method generates an intervention comprising a suggested modification corresponding to a variable of a selected causal model among the candidate causal models in the re-ranked list, receives additional data corresponding to the variable of the suggested modification and evaluates the additional data to determine whether the likelihood of the selected causal model has changed.

Abstract: A method implemented by a processor of a device, the method including determining one or more user values, determining a prompt requiring a decision, and receiving a user selection to one or more choice options related to the one or more user values and pertaining to the prompt requiring a decision. The method further includes comparing the user selection to a risk-neutral utility function and assessing a risk of a potential decision based on the comparison.

Abstract: Electronic devices and methods for displaying information on electronic displays of electronic devices based on learned user behavior are disclosed. In one embodiment, an electronic device includes an electronic display, a processor, and a non-transitory computer-readable medium storing instructions that, when executed by the processor, causes the processor to cause for display on the electronic display a selected representation of information among a plurality of representations based at least in part on learned behavior of a user.

Abstract: Systems and methods for selecting a channel of communication based on a type of thinking employed by a user for a task are disclosed. The systems and methods include determining that a user is employing System 1 type thinking or System 2 type thinking for the task based on one or more properties of the task, one or more properties of the user, and a state of the user based on physiological response data from one or more physiological sensors monitoring the user, and implementing a channel of communication to utilize for the task that corresponds to a determined type of thinking the user is employing for the task.

Abstract: Systems and methods for determining whether a user employs System 1 type thinking or System 2 type thinking when engaged in a task are disclosed. The systems and methods include determining one or more properties of the task based on information regarding the task received from a database storing information regarding the task, determining one or more properties of the user with respect to the task, determining a state of the user based on one or more physiological sensors configured to sense one or more characteristics of the user, and determining that the user employs System 1 type thinking or System 2 type thinking when engaged in the task based on the determined one or more properties of the task, the determined one or more properties of the user, and the determined state of the user.

Abstract: A method for generating and outputting a prompt for performing a task in a designated time segment is provided. The method includes obtaining, from a plurality of sensors, context data associated of the user related to time segments, categorizing each of the time segments into one of a plurality of thought states based on the context data, mapping a task from a task dataset associated with the user into one of the plurality of thought states, and generating a prompt for performing the task during a designated time segment of the time segments, the designated time segment corresponding to the one of the plurality of thought states to which the task is mapped.