Game Theory

Game Theory in many respects is the bedrock of advertising and algorithmic trading markets. Combine this with Complex Networks and formal Machine Learning and one has a decisive strategy model. The mathematical models are also applied in Multiagent Systems for studying Argumentation Theory and communication between agents. A Beautiful Mind was a movie that perhaps made Game Theory and the concept of Nash Equilibrium a more mainstream concept. There are endless applications to the field and a few reference sources of further study are provided below.

Graph Theoretic Methods in Multiagent Networks
Multiagent Systems
MAS: Algorithmic, Game-Theoretic, and Logical Foundations

Algorithmic Game Theory

Game Theory: An Introduction

Mathematical Game Theory and Applications
Networks, Crowds, and Markets

Coursera: Game Theory

Coursera: Game Theory II : Advanced Applications

Complex Networks

The rising scale of data and the need for information gain has provided a greater need towards understanding patterns to form knowledgeable insights. In many cases, such patterns can be derived through machine learning and data mining. But, also through studying complex networks that form within contextual data. The below links provide useful sources of study in the science of complex networks.

The Structure and Dynamics of Networks

Networks, Crowds, and Markets

Networks: An Introduction

Understanding Social Networks

Six Degrees: The Science of Connected Age

Coursera: Networked Life

Coursera: Social Network Analysis

Network Science

Network Science Book

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Brexit Pipeline

Studying Sentiment Analysis in context of Brexit (EU Referendum) is currently an intensive area as the polling stations will very soon be active for voters. Input sources from social media and news feeds can be a focal point for storytelling about the various events. Social media and news feeds can be utilized in form of stream processing which can then be used for machine learning analysis and then indexed for summary into Elasticsearch. A sample workflow example is provided below.  Reader will take notice that the sample workflow is also supported as an example for learning Apache Flink. The workflow can be modified as required for example, one could use a Redis cache layer between the machine learning process and Elasticsearch. Also, could extend with an NLP pipeline (Gate/UIMA) or simply OpenNLP/CoreNLP for extracting information. One could even replace Apache Flink with Spark or GraphLab. Alternatively, one could even replace Kafka with Kinesis and simply apply the AWS data pipeline. Also, the data sources can be stored using S3. Furthermore, one could even use DL4J with Spark on ElasticMapReduce to apply Deep Learning approach in form of convolutional neural network model. Although, Python developers may be more inclined to use Theano, TensorFlow and possibly RabbitMQ. For a graph representation one could use Titan, GraphX, Elasticsearch Graph, Cayley, PowerGraph, Gelly, among others. As one can see there are several ways of implementing a solution on a case-by-case basis to translate the requirements of stories. However, prototype in small is always the best way to go before scaling out incrementally i.e fail fast. 

Input->Kafka->ApacheFlink->Elasticsearch->Output

Steps:

1. Collect
2. Log 
3. Analyze
4. Serve & Store

List of Input Sources:

• Twitter
• Youtube
• Google+
• Facebook
• Tumblr
• IntenseDebate
• Disqus
• Blogger
• YouGov
• Reddit
• NewsFeeds (Reuters, Digg, HuffingtonPost, BBC, Guardian, Telegraph, Independent, Google News, AP, TheWeek, etc)
• EUReferendum
• EU-referendum
• Fullfact
• InFact
• UK & EU
• Breakingviews
• Pollstation
• UK Cabinet Office
• Economist
• Whatukthinks
• Wikipedia
• NCpolitics
• Survation
• ICM
• ORB
• Opinium
• BMG Research
• Ipsos-mori
• UKpollingreport
• MigrationWatch
• European Central Bank
• EconomicsNetwork
• Global Research
• ...and others

As a side note, GNIP and DataSift provide an entire data source pipeline for building out a firehose of streaming inputs. Live Polling data can also be used to gather voting trends as they happen. However, as the referendum is now past, one can probably get a hold of the dataset or API.

Deep Learning Software

Below are a few links to current deep learning libraries available for the developer community.

software links
deep learning libraries by language
popular deep learning tools
15 Libraries for Deep Learning
Comparison of Deep Learning Software
deep learning bibliography

Intelligent Health Insurance

Private Health Insurance needs to be transparent with claims handling. They also need to profile their clients ethically and cover for legitimate claims. But, many private health insurance companies even lack basic customer service. Some providers are so irresponsible that they even make it difficult for clients to contact them directly. Increasingly, it seems many providers avoid to cover for legitimate claims. In what respects are they being regulated for such practices. CrowedFunded Insurance providers should be the next step forward. This way everyone can be a customer as well as a health provider. Collectively, everyone is reaching a win-win situation rather than a single insurance provider that is driving business out of the medical misery of others. This may also increase medical accessibility for all concerned as well as improve research. Furthermore, deep analytics can be attained on health profiles on the basis of handling claims. Such practices can further be shared in form of peer-to-peer networks between countries so people can take advantage of quality and cost-effective healthcare in a timely fashion without having to wait in a queue or being limited on the choice of a medical professional. However, stringent regulatory and compliance requirements would have to be met. One might wonder that the NHS is partly doing this already. However, the NHS is badly managed, deeply seated in bureaucracy, and some times uncaring medical staff. Also, doctors can directly converge on claims handling avoiding malpractice and increasing the scope for specialist consultation. DNA profiling can also be a step forward towards healthy living for the future as well as personalization in healthcare. The driving force for any healthcare should be that the health of the patient comes first, above all they receive the most appropriate treatment and diagnosis, in a timely fashion, as should be expected without an excessive financial burden.

Asynchronous Communication Approaches

asynchronous communication in soa/msa

Reasoning in Artificial Intelligence

Reasoning is an important aspect of knowledge representation in artificial intelligence. It provides a formalism for deduction and induction over representation of a set of knowledge utilizing constraints for inference defined in form of logical rules. Hence, why knowledge representation and reasoning are so interrelated in theory. There are various forms of reasoning in knowledge-based systems. A few reasoning approaches are listed below. 

Analogical Reasoning

Deductive Reasoning

Inductive Reasoning

Abductive Reasoning

Fallacious Reasoning

Meta-Reasoning

Probabilistic Reasoning

Commonsense Reasoning

Rule-Based Reasoning

Case-Based Reasoning

Constraint-Based Reasoning

Approximate Reasoning

Procedural Reasoning
Spatial-Temporal Reasoning