Why Centralization Should Be Avoided standard

By: Francois Aubin. Centralization, while intended to standardize processes and achieve economies of scale, centralization presents significant drawbacks that hinder effective work organization, particularly in decision-making processes. It requires local units within an organization to comply with standardized policies and rely on decisions made by a central authority. This structure restricts local units from making context-specific decisions, leading to frustration and inefficiency.   The Pitfalls of Centralization Lack of Autonomy: Centralization removes decision-making power from local units, despite these units having the most relevant information about their situations. For example, employees at a local branch might understand their specific challenges and opportunities better than a distant central office. However, centralized policies prevent them from acting on this knowledge, which is ...

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The Business Banker Loan Origination Software: Optimizing Credit Granting standard

In the banking sector, the process of granting credit is essential. Business Banker has developed a rigorous decision workflow that is both easy to use and to configure to manage this critical aspect, effectively evaluating credit applications, minimizing financial risks, and ensuring fail-safe regulatory compliance. Foundations of the Decision Workflow: Client Information:The process begins by categorizing clients (individuals, SMEs, large enterprises, financing entities, cooperatives), with each segment requiring a tailored approach strategy. Integrating the client into our systems necessitates identity authentication and the collection of specific information through a comprehensive KYC (Know Your Customer) process. Financing Request:Tailored to the client’s specifics, the request includes: Credit facilities, Collateral securities, Disbursement terms, Obligations to be met. Specific Risk Model: Each client segment ...

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Part 2: Dirac’s reasoning on the discovery of antimatter standard

By: Francois Aubin. Summary: Cognitive Engineering examines individual interactions and decision-making in technological contexts, emphasizing human reasoning dimensions like information processing, judgment, and problem-solving. This study highlights cognitive skills fundamental to reasoning, including pattern recognition, memory, abstract thinking, and logic, using Direct’s theories.  Cognitive Engineering:The aim is to automate and design better systems by focusing on understanding how individuals interact with technology and make decisions in complex systems. This field scrutinizes the ways in which people process information, make judgments, and tackle problems. The ultimate objective often revolves around enhancing human-machine interaction and refining decision-making processes in environments driven by technology. Human Reasoning:Human Reasoning is the process of drawing inferences or conclusions from established facts and premises. This ability is ...

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Part 1: Albert Einstein’s Superior Reasoning Capacity standard

By: Francois Aubin. Summary: Cognitive Engineering examines individual interactions and decision-making in technological contexts, emphasizing human reasoning dimensions like information processing, judgment, and problem-solving. This study highlights cognitive skills fundamental to reasoning, including pattern recognition, memory, abstract thinking, and logic, using Albert Einstein’s theories as exemplary applications. Cognitive Engineering:The aim is to automate and design better systems by focusing on understanding how individuals interact with technology and make decisions in complex systems. This field scrutinizes the ways in which people process information, make judgments, and tackle problems. The ultimate objective often revolves around enhancing human-machine interaction and refining decision-making processes in environments driven by technology. Human Reasoning:Human Reasoning is the process of drawing inferences or conclusions from established facts and premises. This ability ...

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Investing in Technology: A Strategic Approach for Organizations standard

By: Francois Aubin. Topics: Procurement of enterprise software, Open source, Cognitive Engineering. Summary Software costs can be reduced by 70% to 90% when using open source instead of enterprise alternatives. While it requires the engagement of developers with specialized skills, leading to additional costs, the overall economic benefits are considerable. This cost efficiency primarily stems from its free-to-download nature, sparing businesses the expense of funding the extensive research and development typically undertaken by enterprise vendors. Furthermore, open source software provides enhanced scalability, adeptly adapting to a business’s evolving needs.   The IT Procurement Process in Large Organizations In the realm of modern business, organizations are increasingly relying on technology investments to stay competitive. This typically includes expenditures on enterprise software ...

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Flying Blind: The Perils of Relying on Machine Learning Without Accurate Data standard

Summary: The most advanced machine learning can produce inaccurate results if the problem is not defined correctly. This is highlighted in the scheduling application for aviation companies. Pilots were unsatisfied due to the flawed algorithm. Introduction: Building schedules for large aviation companies can be a complex task that involving various factors: Seniority, regulations, individual preferences and routes for thousands of pilots and crew members.  It is crucial to integrate all the factors correctly to create a fair and efficient schedule that satisfies everyone. User experience review: The scheduling application was evaluated by conducting one-on-one interviews and observations with 20 pilots from five aviation companies: Delta, United, Air Transat, Air Canada.  The pilots found the application frustrating to use.  They felt ...

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The Illusion of Expertise in Open-Loop Systems standard

Summary: This article debunks the erroneous beliefs regarding the expertise of individuals in making decisions within open-loop systems and presents an approach to overcome their limitations. Open-loop systems don’t provide real-time feedback of the results.  This makes it difficult to adjust strategies effectively.  For instance, in real estate investments or marketing campaigns, the success or failure of a decision is not known for months or years, making it difficult to make informed decisions.  For example, human resource managers receive feedback on an employee’s performance after making the hiring decision . The lack of actual feedback in open-loop systems impairs the accumulation of experience.  Unlike close-loop systems, like driving a car, where numerous decisions are made with real-time feedback.  Open-loop systems ...

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AI and Human Decision Making: A Winning Combination standard

Discussions on how AI can improve human decision-making by providing accurate and up-to-date information and knowledge. Human Reasoning Reasoning is the process through which individuals use information and knowledge to make judgments, decisions and solve problems. However, it is surprising to hear people expressing opinions on topics for which they have limited information or knowledge. Questions about the Vietnam War, the JFK assassination, global warming or vaccines receive diverse responses from individuals. Further questioning will reveal that their knowledge on the subject is insignificant. For example, some people determine that climate warming is a natural phenomenon, not caused by human activities. They argue that there were cold and warm periods in the past, volcano emissions are more significant than human ...

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Myth regarding battery electric vehicle versus internal combustion engine standard

Myths have played an important role in human history by providing moral guidance and creating a sense of identity and community. Myths can also be a cognitive limitation for humans and can lead to wrong decisions such as war. Why human believe in myths: They may lack access to accurate information or the skill to evaluate the information they have. They often apply their previous experiences and knowledge to new situations. They might reject information sources because they disagree with the ideology presented by those sources. Cognitive biases that make it difficult to question or challenge myths. For example, people may be more likely to accept information that confirms their existing beliefs and to reject information that contradicts them.  They ...

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Why Data Science Projects Fail and How Cognitive Task Analysis Can Prevent It standard

Key causes of failures No good state of the end goal of a project. The desired outcome that is hoped to be achieved by completing the project.  Poor-quality data can lead to wrong conclusions and unreliable results due to lack of research, not properly assessing the data available. Lack of communication. Data science projects are complex and require collaboration between data scientists, engineers, and stakeholders. Results can lead to misunderstandings and incorrect assumptions being made.  Poor Modeling: If the wrong model is chosen or the model is not fine-tuned properly, the project may not yield the desired results.  How cognitive task analysis can ensure success of data sciences projects Identify what the problem project is intended to solve and create ...

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