Quelles sont les stratégies de résolution de problèmes les plus courantes qui nécessitent des compétences de raisonnement logique ?

Analysis and Decomposition: Break down complex problems into smaller, more manageable parts to analyze and solve them systematically. Critical Thinking: Evaluate information objectively, considering multiple perspectives and potential solutions. Deductive Reasoning: Draw specific conclusions from general principles or premises, applying logical steps to reach a solution. Inductive Reasoning: Make generalizations or predictions based on specific observations, patterns, or trends. Abstraction: Algorithmic Problem-Solving: Root Cause Analysis: Decision Trees: Syllogistic Reasoning: Logic Models: Hypothesis Testing: Proofs and Justifications: Causal Reasoning: Venn Diagrams Boolean Logic Constraint Satisfaction: Proof by Contradiction

Drawing on my experience as a seasoned Business Analyst, common problem-solving strategies requiring logical reasoning include root cause analysis, SWOT analysis, and process mapping. These methodologies involve systematically breaking down complex issues into manageable components, allowing for precise identification of problems and formulation of strategic solutions. Additionally, logical frameworks, decision trees, and data-driven analysis are instrumental in deducing patterns and making informed, rational decisions. My experience showcases the application of these strategies to navigate intricate business challenges effectively.

Inductive reasoning is another powerful logical problem-solving strategy. It involves deriving general principles from specific observations. For instance, observing several instances of a phenomenon and concluding a general pattern characterizes inductive reasoning. This approach is valuable for generating hypotheses, forming theories, and predicting outcomes. To effectively use inductive reasoning, ensure your observations are representative, varied, and systematically analyzed, allowing you to make informed generalizations based on accumulated evidence. Integrating both deductive and inductive reasoning enhances your analytical skills, enabling a comprehensive and balanced problem-solving approach.

Cuando te encuentres ante un desafío, como un equipo que no logra alcanzar sus metas de productividad, aplica tu habilidad en el razonamiento deductivo. Desde principios generales, desglosa cada componente del equipo, eliminando posibles causas hasta llegar a la raíz del problema. Este enfoque no solo resuelve la situación actual, sino que también establece un sistema sólido para abordar desafíos similares en el futuro.

Quando as premissas são verdadeiras, a dedução é uma forma coerente de inteligibilidade sobre as coisas! Porém, as premissas nem sempre são verdadeiras! Ex:Todo homem( ser humano) é bom, João é homem, logo João é bom! Porém , João pode não ser bom! A premissa denominada de maior ( a principal que traz a afirmação- todo homem..) que afirma que todo homem é bom, não necessariamente é verdadeira . Pois, com exceção dos leitores e adeptos de Rousseau, que acreditam que o homem é bom por natureza , os demais não necessariamente acreditam nesta teoria. E, não acreditam frente a todo tipo de maldade que muitos homens cometem, especialmente aqueles que são psicopatas. Frente a esta possibilidade de erros, a dedução lógica nem sempre é tão lógica.

Supongamos que te enfrentas a una alta tasa de rotación de empleados. Utiliza tu capacidad de razonamiento inductivo para observar patrones y tendencias en los datos. Al analizar las razones detrás de las salidas, identifica temas comunes. Esta observación te permite formular soluciones basadas en las tendencias identificadas, abordando no solo los problemas actuales, sino también previniendo futuras pérdidas de talento.

In my experience inductive reasoning is one of the most common logical reasoning an analyst will use. More often analyst cannot explore all data points one by one so the best way is to sample some data points to analyze and understand and the use that understanding to induce the behavior of the total population.

In data analysis, the prominence of inductive reasoning, extracting general principles from specific observations, is undeniable. While beneficial for pattern recognition and hypothesis formulation, its reliance on data quality poses challenges. Incomplete or poor-quality data may lead to erroneous patterns and, consequently, flawed decision-making. Balancing inductive reasoning with deductive approaches and prioritizing data quality becomes paramount for analysts to derive accurate insights and make informed decisions in dynamic real-world scenarios.

Abductive reasoning involves forming the most likely explanation for observed phenomena, even if it's not guaranteed to be true. It emphasizes generating hypotheses that best fit the available evidence. This perspective is valuable in navigating uncertainty, fostering creativity, and aiding decision-making when faced with incomplete information, making it an essential aspect of logical thinking.

Cuando te enfrentas a un escenario complejo, como la falta de motivación en un equipo, recurre al razonamiento abductivo. Observa el comportamiento y el contexto, proponiendo varias explicaciones posibles para la falta de motivación. Luego, elige la explicación más plausible basándote en la evidencia y la comprensión contextual. Este enfoque creativo te permite abordar problemas ambiguos de manera efectiva.

Using educated guesses to infer the best possible solution for a problem. You have a problem and the process of you coming up with the most plausible solution is called abductive reasoning.

In abductive approach, the research process starts with ‘surprising facts’ or ‘puzzles’ and the research process is devoted their explanation. ‘Surprising facts’ or ‘puzzles’ may emerge when a researchers encounters with an empirical phenomena that cannot be explained by the existing range of theories.

Analogically reasoning involves identifying the relations between two different scenarios and draw conclusions based on it , the insights that are drawn must be valid and sound. For instance, reading a book and watching a movie might seem similar activities as both involves storyline but understanding the plot is where the catch is.

Imagina que te enfrentas a la tarea de mejorar la colaboración interdepartamental. Utiliza tu habilidad en el razonamiento analógico al buscar situaciones similares que hayan sido resueltas con éxito. Identifica analogías en contextos diferentes y aplica soluciones probadas en nuevos escenarios. Este método innovador te permite abordar desafíos con una perspectiva fresca y eficaz.

Analogical reasoning (AR) involves discerning and leveraging similarities between disparate domains or cases. This reasoning technique isn't just about finding commonalities but also entails a deep understanding of relevant characteristics & differences. For ins., drawing parallels between chess and other board games to facilitate learning exemplifies AR. It's pivotal in assimilating new concepts, applying known knowledge & fostering creativity.Its efficacy lies in accurately identifying pertinent features & similarities while cautiously steering clear of deceptive or erroneous analogies. AR serves as a powerful tool in our cognitive arsenal, bridging the gap between the known & the unknown, & aiding in complex problem-solving & innovation.

Analogical reasoning is a form of logical reasoning that uses similarities or analogies between different cases or domains to transfer knowledge or solve problems. For example, if you want to learn how to play chess, you can use analogical reasoning to compare it to other board games that you know. Analogical reasoning is useful for learning new concepts, applying existing knowledge, and generating creative ideas.

In my experience, in problem-solving, logical reasoning is crucial. Some common problem-solving strategies that involve counterfactual reasoning include decision trees and scenario analysis. Constructing decision trees involves mapping out different possible decisions and their consequences. Counterfactual reasoning comes into play when assessing the outcomes if decisions had been different at key points. In addition, systematically analyzing various scenarios allows for consideration of alternative paths and outcomes. These problem-solving strategies, rooted in logical reasoning and counterfactual thinking, are valuable tools for navigating complex situations and making informed decisions.

O método de raciocínio contrafactual é o meu preferido, porque é aquele famoso ditado "Aprenda com os seus erros". Utilizando essa lógica, você passa a enxergar seus erros como oportunidades de aprendizado, em lugar de sentir culpa ou arrependimento. É claro que o erro nos causa um pesar e não devemos deixar de viver esse sentimento, mas entenda que é importante não ser consumido por isso. Lembre que tudo passa e avalie a situação para que você possa melhorar na próxima vez.

Counterfactual reasoning is especially useful in business analysis when forecasting. For example you would want to know the implications of a price increase of your product. Counterfactual reasoning can let you know that for instance a 20% increase in your price will lead to a 10% loss in customers.

You need a broad view , like if I share a incident once a big truck got stuck under a bridge as hieght of bridge was low and truck was big, by wrong estimate truck driver thought we can pass that but during the course they got stuck. Now to get the truck out safely many engineers and people gather together they combinedly got a solution to break that part of bridge that will make some space and then we will pull the truck backside . This solution was Ok but lots of expense . Now a little 14 year old boy who was just crossing the road saw this and shouted to main Engineer,Hey can we just not remove air from truck tire,and truck will automatically get some free space then just drive it back. Every one was stunned 😯. So that borad view power.

This article came across me first.  While I was thinking about what I could say about it, another article suddenly popped into my head: “How to get the most out of your data analysis internship?”. "Now everything is in its place." The subject that makes reasoning possible is the subject of "data and data science". The mind can only perform an operation thanks to data. So, can't there be any action in the mind when there is no data? The way it processes data is reasoning. If there is reasoning, there is data.  However, the value of having data in the mind is only if the mind processes it.  And if we tell the mind how it should process it. This article has become a tip to the mind.

In addition to above there are some other common problem-solving strategies that require logical reasoning skills are: 1. Analyzing the problem: Breaking down the problem into its components, identifying relevant information, and understanding the relationships between different elements. 2. Creating logical frameworks: Developing systematic frameworks, such as decision trees or flowcharts, to organize information and guide the problem-solving process. 3. Considering alternative solutions: Generating multiple possible solutions and evaluating their logical coherence before selecting the most suitable one. By utilizing these problem-solving strategies, individuals can effectively approach complex problems and arrive at logical solutions.

Deductive reasoning, akin to a detective's method in solving a mystery, is an intriguing strategy. By starting with general truths and applying them to specific cases, remarkable insights can emerge. Another, inductive reasoning, where specific observations lead to general conclusions, much like a scientist discovering a new law of nature. Abductive reasoning, less conventional but equally compelling approach, is likened to a doctor's diagnosis based on symptoms. These strategies, while diverse, all share a common thread: they require the skillful application of logic to navigate from known information to new discoveries and solutions.

Another important strategy is Root Cause Analysis (RCA). RCA is a methodical approach for identifying the underlying causes of problems or events, aimed at preventing future recurrences. This process involves defining the problem, gathering detailed information, and constructing a timeline of events. Techniques like the "5 Whys", Fishbone Diagrams, and Failure Mode and Effects Analysis are employed to differentiate between causal factors and the actual root cause. Once identified, the root cause is addressed through specific, actionable solutions. RCA not only helps in resolving the immediate issue but also enhances the overall understanding of the system or process involved, leading to improved quality and efficiency.