Description:DECISION-MAKING SIMULATION PLATFORM

FIELD OF INVENTION
The present invention relates to a no-code platform for creating business simulations, incorporating adaptive branching and real-time feedback. It allows USER (SC) to design, create, modify, and execute simulation scenarios without programming knowledge. The present invention includes a real-time computational logic engine, automated scenario branching, multi-layered variable dependency management, and adaptive feedback and scoring mechanism. The system increases simulation precision and responsiveness by implementing decision chains and instant feedback to the USER (SP). Business games are commonly utilized in training, business education, and strategic planning. The systems in use are, however, based on programming skills and not responsive in real-time, thereby being ineffective. The present invention solves these issues by providing an accessible platform that automates scenario development and feedback while preserving accuracy through recursive processing and dynamic variable management. The present invention is applicable across various industries, including corporate training, business strategy development, and educational programs.

BACKGROUND OF THE INVENTION
Business simulations have emerged as a critical decision-making tool for business leaders in many industries, allowing them to simulate and analyze intricate business situations in a risk-free environment. The business simulations are essential for strategic decision-making, operational optimization, and market forecasting, enabling business leaders to analyze complex scenarios in a risk-free environment. These simulations use real-time data processing, AI-driven predictive analytics, and dynamic scenario branching to assess potential outcomes. However, traditional simulation tools require programming expertise, rigid scenario structures, and high costs, making them inaccessible to non-technical users and small businesses. Conventional platforms depend on coding skills for defining logic, updating simulations, and integrating external data, limiting usability. Additionally, they often lack real-time adaptability, preventing dynamic adjustments based on user inputs.

In addition, traditional simulation software is usually not flexible enough to adapt dynamically to shifting simulation paths and results in accordance with immediate USER (SP) feedback and changing data. Lack of adaptation in simulations to variable changes or the inclusion of adaptive feedback leaves a hole in the capability to generate actionable insights and enhance decision-making in real time.

US11285378B1 discloses a business-simulation board game kit for a multi-player game simulating a business and/or economy comprising a plurality of collections of playing pieces, and a game board. Each collection of playing pieces is assigned to a corresponding player of a plurality of players. Each collection of playing pieces comprises a plurality of playing pieces representing a business or economy comprising a plurality of business segments.

KR101109865B1 discloses an enterprise simulation providing system and a method of providing the same. More specifically, the learners participating in the simulation simulate key management decisions such as business strategy, marketing, production, finance, etc. while simulating management performance values accordingly.

Current solutions also have difficulties dealing with interdependencies among various simulation variables, and it is hard to assure uniform outputs when variables change. A lack of automatic resolution mechanisms for conflicts arising between these interdependencies tends to lead to errors or faulty simulation outputs. Most platforms do not provide a simple-to-use interface, and this complicates simulation designing and changing for USER (SC)s who are not technically inclined.

There is a need for an intuitive no-code business simulation platform that enables USER (SC)s to build, change, and run business simulations without programming expertise. The platform must be able to adapt to the USER (SP)'s real-time decisions, include feedback dynamically, and maintain interdependent variables' consistency throughout the simulation. In addition, the site must provide reporting features that deliver actionable insights and performance analysis so that USER (SP)s can sharpen their strategies and maximize their decision-making.

The present invention delivers the solution by providing a dynamic, no-code business simulation platform that can overcome these issues. The platform has a no-code USER (SC) interface where USER (SC)s can specify simulation logic, decision nodes, and rules for branching using a graphical interface, which makes it easily available to non-technical USER (SC)s. It has a computational logic engine that can handle data in real-time and an adaptive scenario branching module that dynamically adjusts the paths in the simulation depending on USER (SP) input. The platform also incorporates an advanced dependency management system to maintain consistency between interdependent simulation variables, a feedback and scoring module that offers real-time performance analysis, and a recursive processing unit that maximizes simulation processing efficiency.

By facilitating real-time decision-making, adaptive scenario paths, and dynamic performance feedback, the platform enriches the simulation experience, providing USER (SP)s with an intuitive and flexible tool to enhance their business strategies. Furthermore, its built-in Unified Reporting System (107) consolidates data from multiple simulations and generates detailed performance reports covering profitability, market share, efficiency, and strategic influence, providing users with comprehensive feedback on their decisions. By integrating results across diverse scenarios, the system enables multi-dimensional analysis, allowing users to compare outcomes under varying market conditions and business strategies. Additionally, it uses predictive analytics to forecast trends, optimize strategies, and enhance decision-making accuracy, making it a valuable tool for Simulation Players (SPs) and Simulation Creators (SCs) in refining their approaches and improving business outcomes. The present invention serves to meet the demand for an affordable, real-time, and extremely flexible business simulation platform, opening simulation tools to more USER (SP / SC)s.

OBJECTS OF THE INVENTION
• The principal object of the present invention is to make business simulation by eliminating the need for programming skills.
• Another object of the present invention is to enable real-time adaptation of simulation pathways through automated branching based on USER (SP) input.
• Yet another object of the present invention is to enhance simulation accuracy by implementing a multi-layered dependency management system.
• Still another object of the present invention is to provide real-time feedback and scoring to improve USER (SP) engagement and learning outcomes.
• An additional object of the present invention is to optimize processing efficiency through recursive processing of decision trees.
• Final object of the present invention to provide comprehensive reporting for multi-game comparative analysis through a unified multi-game reporting feature.

SUMMARY OF THE INVENTION
The present invention provides a no-code, dynamic business simulation platform that allows USER (SC)s to create, edit, and run business simulations without the need for programming. The platform consists of several primary modules that interact in a collaborative effort to build and modify simulation scenarios according to real-time USER (SP) feedback and decisions to provide a dynamic and adaptive simulation environment. These modules consist of a no-code USER (SC) interface, a real-time computational logic engine, an automated scenario branching mechanism, a multi-layered dependency management system, an adaptive feedback and scoring mechanism, a recursive processing unit, and an unified reporting system. USER (SC)s can create business scenarios, define on choices, and design the outcomes using the platform with nil or minimal technical expertise, facilitating ease of use.

Key Features of the Invention:
No-Code USER (SC) Interface (101): The no-code USER (SC) interface (101) is the USER (SC)s' main interface for developing and editing business simulation scenarios without requiring programming skills. It offers a graphical, USER (SC)-friendly interface for specifying/defining decision points, simulation logic, and branching rules, which can be edited with a number of different functionalities of Graphical Interface Editor. This makes the platform very USER (SC)-friendly for non-technical USER (SC)s.

Real-Time Computational Logic Engine (102): The computational logic engine (102) runs USER (SP) inputs in real-time and dynamically performs the simulation logic. It is charged with assessing USER (SP) decisions and modifying the simulation paths and results based on the actions of the USER (SP). The engine makes sure that the simulation gives instant feedback, which is crucial for interactive decision-making.

Automated Scenario Branching Module (103): The automated scenario branching module (103) adjusts the simulation path dynamically based on USER (SP) input and real-time information. The module alters the simulation flow and reconfigures branching results according to a rule set or probabilistic evaluation. It provides USER (SP)s with a highly adaptive simulation where every decision has the potential to result in a different set of outcomes.

Multi-Layered Dependency Management System (104): The multi-layered dependency management system (104) ensures consistency of simulation variables at various levels of interdependency. It facilitates the automatic update of dependent variables when parent variables change, and this ensures the integrity of the simulation. This system is necessary for handling simulations in which numerous variables coexist and impact one another.

The system enables the seamless development of complex, interactive simulations without requiring programming skills, powered by a Dynamic Branching Engine for real-time scenario evolution and a Variable Management Module for maintaining logical dependencies. The Dynamic Branching Engine ensures that every decision dynamically influences the simulation’s trajectory, integrating automated decision pathways, probability-based outcomes, nested branching mechanisms, and AI-powered adaptability to create fluid, responsive scenarios. It instantly evaluates user inputs, modifies conditions based on probabilistic models, and refines future decision options by learning from user behaviour. Complementing this, the Variable Management Module maintains hierarchical variable relationships, automated data propagation, multi-layered dependency tracking, and real-time synchronization, ensuring logical coherence across scenarios. By linking interdependent variables such as how pricing affects demand, supply chain efficiency, and profitability the system ensuring realistic cause-and-effect relationships. Additionally, its conflict resolution mechanism detects and corrects contradictions, such as attempting to expand production while cutting operational costs. Together, these components create a dynamic, data-driven simulation environment that adapts to user decisions, making the platform an essential tool for strategic planning, corporate training, and business education.

Adaptive Feedback and Scoring Module (105): The adaptive feedback and scoring module (105) calculates real-time performance scores and gives feedback based on USER (SP) choice and the simulation results. It assesses USER (SP) actions based on predetermined performance measures and assigns weighted scores to results. This module also creates personalized recommendations for performance improvement based on the simulation results.

Recursive Processing Unit (106): The recursive processing unit (106) makes simulation processing more efficient by caching and reusing computed results. This minimizes computation by not repeating the same calculations, hence making the platform more efficient, particularly in intricate or repetitive simulations. It can also use machine learning algorithms to learn from USER (SP / SC) patterns and alter future results based on that.

Unified Reporting System (107): The Unified Reporting System (107) integrates data from multiple simulation games, each containing several scenarios, and generates detailed performance reports. These reports cover aspects such as profitability, market share, efficiency, and strategic influence, providing USER (SP/SC) with comprehensive feedback on their decisions. The system consolidates and presents data from various simulation games, enabling a thorough performance evaluation across different game contexts and scenarios By integrating results from various simulations, the system provides actionable insights, helping users assess decision-making effectiveness, optimize strategies, and identify patterns in their business simulations. This feature is particularly beneficial for Simulation Players (SPs), who can compare their performance across different game scenarios, and Simulation Creators (SCs), who can analyse user behaviour to refine simulation models. The reporting system supports multi-dimensional analysis, allowing users to compare outcomes under different market conditions and business strategies. Additionally, it incorporates predictive analytics, leveraging historical data to forecast future trends and guide strategic decision-making. By offering a unified framework for simulation analysis, the system enhances decision-making accuracy, streamlines performance assessment, and improves learning outcomes in business simulation environments.

The system facilitates multi-dimensional analysis and comparison of performance among different simulation scenarios, functional competencies such as strategy planning, functional skill, leadership skills, managerial skills and other skills giving USER (SP / SC)s insights into their strategies and results.

Graphical Interface Editor (108): The no-code USER (SP / SC) interface (101) features a drag-and-drop editor, which allows USER (SP / SC)s to simply define simulation logic and branching rules. The editor makes it easy to create business simulations without needing technical skills. UI elements used are slected from but not limited to:
• Drag-and-drop: This allows users to move or resize objects by clicking and dragging them.
• Point-and-click: This is a fundamental interaction method where users select objects or activate actions by clicking on them.
• Dropdowns: These are lists that allow users to select from a predefined set of options.
• Radio buttons: These are circular buttons that allow users to select only one option from a group.
• Buttons: These are rectangular or circular elements that trigger actions when clicked.
• Text fields: These allow users to enter text input.
• Checkboxes: These allow users to select or deselect multiple options.

In the development of a Business Simulation Game, the process can be significantly accelerated compared to traditional methods. This approach begins with defining the Scenario, which sets the context for the simulation within the game. The next step involves designing the Screen, the user-facing interface that presents the scenario details and interactions. The crucial part of this process is the Layout Design with the Graphical Interface Editor (GIE), where the user arranges the screen elements, such as segments, panels, and controls, to create a fully functional interface. The user can divide the screen into customizable sections and choose the elements—such as text, images, sliders, and buttons—that will populate each section. This method enables rapid development of simulation games by streamlining the interface design, reducing the time spent on manual coding, and offering a more intuitive way to build engaging simulations. With these tools, games that traditionally would take months to create can be developed in a matter of days.

Decision Tree Algorithm: The computation logic engine (102) bases its evaluation of USER (SP) decisions and dynamic simulation outcome adjustments on a decision tree algorithm. This ensures that every choice entered by the USER (SP) is weighed in the light of the simulation, resulting in real-time changes to the flow and outcomes of the simulation.

Probabilistic Analysis for Branching: The computerized scenario branching module (103) makes use of probabilistic analysis to modify branching results in accordance with real-time data and USER (SP) input. This enables the simulation to offer a great range of scenarios depending on uncertain variables, thus increasing the realism and complexity of the simulation.

Hierarchical Conflict Resolution in Dependency Management: Hierarchical conflict resolution logic is used by the dependency management system (104) to resolve conflicts between interdependent simulation variables. This ensures that changes to parent variables are automatically reflected in their dependent variables, keeping them consistent and preventing errors in the simulation.

Weighted Scoring System: The adaptive feedback and scoring component (105) provide weighted scores to simulation results, with emphasis on certain metrics over others in accordance with the USER (SP / SC)'s established performance requirements. This enables USER (SP / SC)s to target goals within their business simulations.

Personalized Recommendations: The feedback module (105) also provides personalized recommendations based on the performance and choice of a USER (SP) in the simulation. These are personalized to assist USER (SP)s in enhancing their decision-making abilities and optimizing their strategies in subsequent simulations.

Caching Mechanism: The recursive processing unit (106) includes a caching mechanism, which keeps previously calculated results and uses them in subsequent simulations. This lightens the computation load and improves the efficiency of the platform by avoiding redundant computations.

Predictive Analysis and Unified Reporting: Predictive analysis reports are produced by the unified reporting system (107) through applying historical data and USER (SP)-specified performance measures. The reports will enable USER (SP)s to make better-informed decisions as they predict the likely outcome on the basis of past performance.

Concurrent Simulation Processing: The computational logic engine (102) is multi-threaded, enabling it to execute multiple simulations simultaneously. This is particularly helpful for large-scale simulations or when USER (SP)s want to execute several scenarios in parallel to compare various strategies.

Nested Branching Mechanism: The scenario branching module that automates (103) contains a nested branching mechanism, permitting multi-layered decision results. This enhances the depth and richness of simulations by enabling USER (SP)s to research several scenarios with various layers of decision-making.

Monitoring and Propagation System: The dependency management system (104) has a monitoring system which recognizes changes to interdependent variables and propagates the adjustments automatically. The system keeps all the simulation variables synchronized and reflects the changes in any section of the simulation across the platform.

Performance Benchmark System: The adaptive scoring and feedback module (105) contains a performance benchmark system, which compares the USER (SP)'s results with historical trends or pre-set benchmarks. USER (SP)s can use this to compare their performance against predetermined standards, assessing their success and areas for development.

The dynamic no-code business simulation platform enables USER (SC)s to create, execute, and modify business simulations with ease, offering a highly adaptable and intuitive environment. The system dynamically adjusts simulation paths based on USER (SP) decisions and real-time feedback, ensuring a responsive and interactive experience. Through its integrated modules, including a computational engine, scenario branching, dependency management, adaptive feedback, and unified reporting, the platform offers powerful tools for business strategy analysis and decision-making.

BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1: Flowchart for no-code business simulation platform
Figure 2: Flowchart for Scenario branching decision flow
Figure 3: Flowchart for Reporting system interaction
Figure 4: Flowchart for Dependency management and feedback integration
Figure 5: Flowchart for USER (SP) interface and logic engine interaction
Figure 6: Flowchart for Complete simulation flowchart
Figure 7: Flowchart for layout design with graphical interface editor (GIE)
Figure 8: Flowchart for Single simulation game report

DETAILED DESCRIPTION OF THE INVENTION
The present invention provides a dynamic no-code business simulation platform that can be used to create, update, and run business simulation scenarios without any need for programming skills. The platform has several connected components, which undertake specific tasks but work together to form an adaptive simulation environment. These functionalities allow USER (SPs)s to simulate intricate business scenarios, make choices, analyse the results, and change their strategies in real-time. The platform is suitable for numerous applications, such as business training, strategic planning, support for decision-making, and market analysis.

The present invention brings business simulation to a broad constituency of USER (SP)s, ranging from business analysts to executives, and offers tools that can simulate scenarios with realistic results. The main features of the platform are a no-code interface, a real-time computation logic engine, an automated scenario branching module, a multi-level dependency management system, adaptive feedback and scoring module, a recursive processing unit, and an integrated/unified reporting system. These features are interdependent to produce a highly versatile system that can carry out intricate simulations based on real-time information and USER (SP) input.

No-Code USER (SC) Interface (101): The no-code USER (SP / SC) interface (101) is the main software for USER (SC)s to design and customize business simulation situations. The no-code design framework enables USER (SC)s without or with minimum programming knowledge to create and adapt simulations using an easy-to-understand graphical user interface.
• The Graphical Interface: The interface is a graphical canvas upon which USER (SC)s can create, edit, and link various simulation elements including decision points, action points, and branch logic. The elements are editable by Graphical Interface Editor (108) selected from but not limited to Drag-and-drop, Point-and-click, Dropdowns, Radio Buttons, Buttons, Text fields, Checkboxes etc., so that USER (SP / SC)s are able to rapidly put together multiple scenarios.
• Decision Points and Branching Logic: Decision points can be created by USER (SC)s, which are critical points in a business process, e.g., market entry, investment decisions, or hiring decisions. Depending on the USER (SP)'s choice, the platform changes the path and results of the simulation. Branching rules can be established that determine how the simulation will proceed based on the USER (SP)'s decisions. For instance, the simulation could diverge in a different way depending on whether a USER (SP) chooses to invest in product development or go for cost reduction.
• Real-Time Feedback: The no-code interface also allows for real-time feedback as the USER (SC)s create the simulation. This way, the USER (SC)s can clearly see the outcomes of their design choices and adapt accordingly to get a better model. The interface has input validation to avoid logical errors so that non-technical USER (SC)s can create valid simulation cases with ease.

Real-Time Computational Logic Engine (102): The computational logic engine (102) is the computing kernel of the platform. It runs the simulation data, performs business logic, and calculates the consequences of USER (SP) choices in real-time. It translates USER (SP) input into executable simulation scenarios and dynamically manipulates the simulation as the USER (SP) interacts with the simulation.
• Simulation Processing: The computational logic engine processes USER (SP) decisions, computes the impact of the decisions, and adjusts the simulation state accordingly. For instance, if a USER (SP) chooses to increase advertising expenditure, the engine recalculates the impact of this on sales, market share, and other associated variables.
• Real-Time Execution: The engine executes inputs and updates results in real-time, providing USER (SP)s with instant feedback on their choices. Real-time processing is critical for interactive business simulations, where USER (SP)s must make choices, see the effects, and modify their plans in real-time.
• Decision Tree Algorithm: In certain situations, the engine employs decision tree algorithms, or similar methods to represent business processes. These algorithms analyze USER (SP) inputs in a structured manner and provide for the simulation to dynamically adapt to the decisions made by the USER (SP) and the changing state of the simulated environment.

Automated Scenario Branching Module (103): The automated scenario branching module (103) performs the task of dynamically changing the simulation paths according to USER (SP) choice and real-time data. The module ensures the responsiveness of the simulation to USER (SP)’s input and gives a variety of outputs based on USER (SP) choice.
• Dynamic Path Adjustment: Depending on the decisions that USER (SP)’s make at different stages in the simulation, the system decides which path to follow based on set rules or probabilistic examination. For example, if a USER (SP) decides to implement a new marketing strategy, the scenario branching module can change the path of the simulation to represent the probable outcomes of the decision, for example, higher customer acquisition or changed market dynamics.
• Probabilistic Analysis: The module can include probabilistic models to determine the probability of various outcomes based on USER (SP) behaviour. This enables the platform to offer USER (SP)s realistic, and outcomes that are contingent on uncertain factors. For instance, the success of a product launch may be determined by external factors such as market trends, competitor actions, and consumer attitudes.
• Nested Branching Mechanism: When dealing with detailed simulations, nested branching is allowed by the scenario branching module. This facilitates layered decisions, as every new choice may create other branching outcomes. This feature extends the simulation so that USER (SP)s can investigate many varying scenarios with different layers of decisions.

Dynamic Branching Engine (103A) for Real-Time Scenario Evolution
The Dynamic Branching Engine is responsible for adapting the simulation flow in real time based on user decisions, ensuring that every choice leads to a unique and evolving scenario. This engine eliminates static, pre-defined pathways and introduces dynamic decision trees that continuously update as users interact with the simulation.
Key Features & Functionalities:
• Automated Decision-Based Pathways – The branching engine adjusts simulation outcomes dynamically, allowing every user decision to generate different consequences and possible future actions.
• Real-Time Conditional Logic Processing – The engine instantly evaluates inputs from Simulation Players (SPs) and modifies the simulation state accordingly. For example, if a player increases product pricing, the simulation adjusts demand, revenue, and market competition in real time.
• Probability & Risk-Based Branching – The system integrates probabilistic decision-making models, enabling outcomes based on statistical likelihoods rather than fixed paths. For instance, a startup funding simulation may determine investor responses based on variable market conditions and prior user choices.
• Nested Branching Mechanism – Supports multi-layered decision-making, where each choice influences subsequent events. If a user expands operations, the system considers factors like logistics, workforce availability, and financial stability before determining the next scenario.
• Adaptive AI-Powered Branching – The system learns from user behavior and can adjust future decision options based on historical choices, refining the complexity of strategic simulations over time.

It ensures that simulations remain fluid, responsive, and strategically immersive, mirroring the uncertainty and interdependencies of real-world business decision-making.

Multi-Layered Dependency Management System (104): The multi-layered dependency management system (104) ensures that there is consistency among interdependent variables in the simulation. Variables tend to rely on each other in any business simulation variables in one location, like the volume of sales, may depend on others, like production cost, personnel, and revenue.
• Parent-Child Relationships: The system creates parent-child relationships between simulation variables. For instance, sales volume may be the parent variable, which affects child variables such as revenue and profit margins. When the parent variable is altered, the dependency management system automatically updates the child variables to ensure consistency in the simulation.
• Automatic Adjustments: The system automatically adapts dependent variables upon change in parent variables. For instance, if a producer wants to boost production capacity, the system may adapt relevant variables, including inventory, manning levels, and operational expenses, automatically. This maintains internal consistency within the simulation and simulates real business dynamics.
• Conflict Resolution: For certain instances, interdependent variables can conflict wherein a change to one variable precipitates conflicting alteration in others. The system utilizes hierarchical conflict resolution logic to detect and resolve conflict, maintaining that the simulation stays logically consistent and error-free.

Variable Management Module (104A) for Dependency Linkages Across Scenarios
The Variable Management Module is important aspect of the system that manages dependencies between simulation variables, ensuring consistency and logical coherence across different decision paths. It enables simulations to dynamically adjust related variables when primary conditions change, maintaining realistic cause-and-effect relationships within the simulation environment.

Key Features & Functionalities:
• Hierarchical Variable Dependency Mapping – Establishes parent-child relationships between variables. For example, if product demand (parent) increases, the system automatically adjusts inventory levels, production costs, and supply chain needs (child variables).
• Automated Data Propagation – Ensures that when one variable is altered, all dependent variables are updated instantly. A decision to reduce advertising spending would dynamically impact brand awareness, customer acquisition rates, and revenue projections.
• Conflict Resolution Mechanism – Detects and resolves contradictory dependencies between variables. If a user attempts to increase production while simultaneously cutting operational costs, the system analyzes constraints and suggests logical adjustments.
• Multi-Layered Variable Tracking – Supports interdependencies across multiple business functions, such as finance, operations, and marketing, ensuring that all variables reflect realistic interactions.
• Real-Time Scenario Synchronization – Maintains consistency across different scenarios, preventing outdated or conflicting data. If an economic downturn affects one market segment, all related scenarios, such as sales forecasting and investment decisions, adjust automatically.

This module enables simulations to react dynamically to user decisions, ensuring logical consistency and data accuracy across all interconnected variables. It enhances strategic realism, allowing Simulation Players (SPs) to experience true-to-life cause-and-effect relationships in business decision-making.

The Dynamic Branching Engine and Variable Management Module work together to create an interactive, adaptable, and data-driven business simulation experience. By automating real-time decision-based branching and ensuring logical consistency in variable dependencies, the No-Code Business Simulation Creation System empowers users to build complex simulations without technical expertise, making it an essential tool for corporate training, business strategy development, and educational programs.

Adaptive Feedback and Scoring Module (105): The adaptive feedback and scoring module (105) calculates real-time performance scores and actionable feedback based on USER (SP) choice and simulation results. This module is crucial for USER (SP)s to assess their progress, recognize the effect of their decisions, and modify their strategies accordingly.
• Performance Measures: The module evaluates USER (SP) choices based on predetermined performance measures, for example, profitability, market share, or efficiency. These measures are given weights depending on the USER (SP)'s goals, and the module computes a score that indicates the USER (SP)'s performance within the simulation.
• Real-Time Feedback: While USER (SP)s make decisions, the feedback module offers real-time performance analysis. For instance, if a USER (SP)'s marketing campaign results in less-than-anticipated sales, the module may offer feedback that the USER (SP) refines the campaign or try something else.
• Custom Recommendations: The feedback module produces customized recommendations dependent on the USER (SP)'s performance. These recommendations are customized to the unique decisions made by the USER (SP) so that they can enhance their strategic thinking and enhance their process to achieve business goals.

Recursive Processing Unit (106): The recursive processing unit (106) streamlines simulation processing by memorizing and recycling earlier computed outcomes. This eliminates the computational burden of re-evaluating repeatedly similar simulation scenarios, enhancing the platform's performance.
• Caching and Reuse: When the platform finds that a previously calculated result has been encountered, the recursive processing unit gets the cached value and reuses it, instead of recalculating the whole scenario. This dramatically accelerates simulation processing, particularly for complicated or recurring scenarios.
• Machine Learning Integration: The recursive processing unit can also use machine learning algorithms to monitor USER (SP) actions and detect patterns of decision-making. This enables the platform to learn and refine the simulation over time, modifying future scenarios according to detected patterns and trends.

Unified Reporting System (107): The Unified Reporting System consolidates and presents data from multiple scenarios of multiple simulation games, enabling a comprehensive performance evaluation across diverse scenarios. By integrating results from various simulations, the system provides actionable insights, helping users assess decision-making effectiveness, optimize strategies, and identify patterns in their business simulations. This feature is particularly beneficial for Simulation Players (SPs), who can compare their performance across different game scenarios, and Simulation Creators (SCs), who can analyse user behaviour to refine simulation models. The reporting system supports multi-dimensional analysis, allowing users to compare outcomes under different market conditions and business strategies. Additionally, it incorporates predictive analytics, leveraging historical data to forecast future trends and guide strategic decision-making. By offering a unified framework for simulation analysis, the system enhances decision-making accuracy, streamlines performance assessment, and improves learning outcomes in business simulation environments. The system facilitates multi-dimensional analysis and comparison of performance among different simulation scenarios, functional competencies such as strategy planning, functional skill, leadership skills, managerial skills and other skills giving USER (SP/SC)s insights into their strategies and results.

Method for User Interface Layer
The following method outlines how the User Interface Layer will function based on the provided requirements. It details how interactive units (such as text, images, inputs, and formulas) can be assigned to specific sections of the screen and adjusted dynamically.

1. Screen Segmentation and Layout
• Screen Division:
o The screen is divided into a flexible grid or sections that can be resized by the user.
o The default screen layout will consist of n x m units, but this can be dynamically resized or split into more granular sections.
o Each segment can be resized, dragged, or repositioned across the screen.
• Unit Size Customization:
o The user can adjust each unit's size from 10% to 100% of the screen width or height.
o The size percentage determines the visual space each unit occupies on the screen (e.g., 50% width or 30% height).
• Repositioning:
o The user can drag any unit to a different location on the screen.
o The layout system ensures that when a unit is moved, it automatically adjusts surrounding units to maintain a coherent layout.

2. Interactive Unit Assignment
• Types of Units:
o The system allows the assignment of the following types of interactive units to each section:
1. Text Units:
? The user can assign plain text or formatted text to a unit.
? Text can be styled (font size, color, alignment) and can be dynamically updated (e.g., through formulas).
2. Image Units:
? Images can be uploaded, resized, and displayed within units.
? Images may also be interactive (clickable, draggable).
3. Input Units:
? Sliders: Users can specify a range (min and max values) and an initial value for the slider.
? Radio Buttons: Users can choose predefined options.
? Text Fields: Users can enter data that is dynamically updated or processed.
4. Formula Units:
? Users can enter simple or complex formulas that are calculated dynamically (e.g., mathematical, financial calculations).
? Formulas are updated in real-time as inputs change (e.g., recalculation on slider change).

3. Unit Customization and Settings
• Unit Attributes:
o Each unit has customizable attributes such as background color, borders, padding, alignment, and font styles (for text).
• Dynamic Adjustments:
o Size: Users can change the size of the unit by dragging the borders.
o Position: Units can be freely moved around using drag-and-drop functionality.
o Content: Units support dynamic content updates. For example, a text unit can change based on user input (e.g., text displayed based on a formula result).
• Input Constraints:
o Input units (e.g., text fields, sliders) can have restrictions for allowable values, such as numeric ranges, predefined text options, or specific formats (email, phone number).

4. Interaction Logic
• User Interaction:
o Text-based units can show static or dynamic content, changing based on system states or user inputs.
o Image-based units can be assigned to any unit area and set to be interactive (e.g., clickable for links, or hover-to-show effects).
o Input fields (sliders, text fields, radio buttons) can trigger actions when users interact with them, such as:
? Changing the values of other units.
? Updating visual content.
? Triggering formula recalculations.
• Formula Calculations:
o Formula-based units dynamically compute and display results based on real-time user input.
o For example, a formula might calculate total price based on quantity and unit price and show it in a text unit.

5. System Adjustments
• Screen and Unit Responsiveness:
o The system ensures that units automatically resize and reposition for different screen sizes and orientations (desktop, tablet, mobile).
o Each unit's size, position, and content are maintained and adjusted according to screen size changes.
• Validation and Error Handling:
o If an invalid input is detected (e.g., out-of-range values for a slider), the unit will highlight the issue, and an error message will be displayed.
o For formula-based units, any invalid calculation or formula errors will also trigger user-friendly alerts.

6. Persistence and State Management
• Saving Layout and Data:
o User configurations (position, size, content) are saved persistently, allowing users to reload their custom layout without losing changes.
o Input data is saved for sessions where necessary (e.g., user form data, slider values).
• State Synchronization:
o Units interact with each other, ensuring that changes in one (e.g., a slider value) dynamically affect others (e.g., text or formula units).

7. Event Handling and Dynamic Updates
• Real-Time Updates:
o Units are updated in real-time when users make changes (e.g., typing in a text field, adjusting a slider, or selecting a radio button).
o For example, when a user changes a slider value, the system recalculates and displays the results in corresponding formula or text units.

This method outlines the key features of the User Interface Layer (UIL), which focuses on flexibility, interactivity, and dynamic screen segmentation. It enables users to create a customized and interactive screen layout, where units can be resized, repositioned, and populated with text, images, inputs, and formulas. The system is responsive and adapts to different screen sizes while ensuring real-time updates and interactions.

The system is also crucial for evaluating the effectiveness of various strategies and making well-informed decisions regarding future activities.
• Comprehensive Performance Reports: The reporting system produces reports that give a complete analysis of the simulation results. These reports can contain key performance indicators (KPIs) like profitability, market share, cost efficiency, and strategic influence. USER (SP)s can analyse these metrics to assess the efficacy of their decisions and identify areas where they need to improve.
• Multi-Dimensional Analysis: The system for reporting offers multi-dimensional analysis, enabling the comparison of different simulation scenarios and evaluation of relative performance. USER (SP)s, for instance, can compare the performance of various marketing plans under different market conditions, yielding a better sense of how their decisions would pan out in real life.
• Predictive Analytics: Along with analysis of historical data, the reporting system can develop predictive reports from historical data and performance metrics defined by the USER (SP)s. This functionality enables USER (SP)s to predict probable results and trends, thus making more strategic decisions.

The Decision-Making Simulation Platform is designed to enhance functional competencies, including strategy planning, leadership, managerial skills, and industry-specific functional skills. Through interactive simulated games and conceptual assessments, users develop real-world decision-making capabilities in a risk-free environment.

The platform supports various simulation-based learning scenarios:
• Business Strategy Simulations help users refine competitive analysis, investment allocation, and risk management skills.
• Financial Simulations strengthen budgeting, forecasting, and profit optimization abilities.
• Supply Chain & Operations Simulations enhance logistics, resource allocation, and operational efficiency.
• Marketing & Customer Behavior Simulations improve strategic planning for branding, advertising, and market segmentation.
• Crisis & Risk Management Simulations build resilience in handling business disruptions and crisis scenarios.

Additionally, conceptual assessments such as decision tree analysis, scenario-based assessments, and AI-driven adaptive learning allow users to evaluate their problem-solving and strategic thinking skills. The platform’s multi-game comparative analysis enables professionals to measure consistency, adaptability, and decision effectiveness across different business environments.

Applicable across corporate training, higher education, entrepreneurship, and government sectors, the platform fosters a data-driven approach to leadership and managerial development, ensuring users can make well-informed strategic decisions and optimize business outcomes.

The dynamic no-code business simulation environment of the present invention provides advancement in business simulation, providing a USER (SP)-friendly, interactive, and highly flexible decision-making, training, and strategic planning environment. By allowing USER (SC)s to create and run simulations without the need for programming skills, the environment democratizes modelling and analyzing complex business situations. With its incorporation of main elements like the no-code USER (SC) interface, computational logic engine, automated scenario branching module, multi-layered dependency management system, adaptive feedback and scoring module, recursive processing unit, and unified reporting system, the platform offers an instrument for modelling real-world business dynamics. These capabilities allow USER (SP)s to make well-informed decisions, analyze their strategies in real-time, and derive useful insights into their business processes without the necessity of specialized technical expertise.

METHOD OF PERFORMING THE INVENTION
The method for performing the dynamic, no-code business simulation platform involves a series of steps that facilitate the creation, modification, execution, and analysis of business simulations by USER (SC)s who do not possess programming expertise. This method ensures that the platform provides an interactive, real-time, and adaptive experience that enables USER (SP)s to model complex business scenarios and make strategic decisions with immediate feedback. The following description outlines the key steps involved in utilizing the platform effectively, from USER (SC) interaction to the generation of simulation reports.

1. Creating a Business Simulation Using the No-Code USER (SP / SC) Interface (101)
Step 1.1: Initialization and Setup of Simulation Environment
• The USER (SC) begins by accessing the platform’s no-code USER (SC) interface (101), which presents a graphical interface that serves as the primary environment for designing and modifying business simulations. The platform should be accessible via a web-based interface or an application with an intuitive, Graphical Interface Editor (108) selected from but not limited to Drag-and-drop, Point-and-click, Dropdowns, Radio Buttons, Buttons, Text fields, Checkboxes etc.
• The USER (SP) is presented with a blank simulation workspace, where they can select predefined simulation elements from a library of components such as decision points, actions, variables, and branching logic along with various UI/UX elements.

Step 1.2: Graphical Interface Design for Scenarios
The platform provides a graphical user interface (GUI) that enables USER (SC) to design simulation scenarios using an intuitive Graphical Interface Editor (108) selected from but not limited to Drag-and-drop, Point-and-click, Dropdowns, Radio Buttons, Buttons, Text fields, Checkboxes etc. The editor allows users to visually organize simulation elements, define logic, and create an interactive simulation environment without coding.

Key Components in the GUI:
1?. Decision Points:
• Represent key junctures where the USER (SP) makes critical choices (e.g., investment strategy, operational adjustments).
• Each decision point links to multiple possible outcomes.
• UI Element: Clickable nodes/icons with connector lines indicating decision pathways.
2. Variables
• Represent dynamic business factors such as sales volume, costs, market share, and customer satisfaction.
• The system tracks and updates these variables in real-time.
• UI Element: Sliders, input fields, and dynamic labels displaying real-time values.
3?. Actions
• Define operational decisions made by USER (SP) (e.g., increasing marketing budgets, reducing staff, launching products).
• These actions influence variable outcomes and branching logic.
• UI Element: Interactive buttons, toggle switches, and action panels for selecting operations.
4?. Branching Logic
• Determines how the simulation progresses based on USER (SP) decisions.
• Uses conditional logic, probability settings, and decision trees to create dynamic pathways.
• UI Element: Flowchart-based connectors, color-coded pathways, and logic indicators (if-then rules, probability sliders).

Below are some of the UI/UX Elements for an Intuitive Experience used in the present invention:
• Drag-and-Drop Functionality – Users can move elements effortlessly into the workspace.
• Point-and-Click Editing – Clicking on elements opens pop-up menus for customization.
• Dropdown Menus – Used for selecting predefined variables and actions.
• Radio Buttons & Checkboxes – Allow selection of binary choices (e.g., "Yes/No" decisions).
• Dynamic Sliders – Adjust numerical values like budgets, pricing, and production levels in real time.
• Tooltip Descriptions – Hovering over elements provides brief explanations and usage tips.
• Color-Coded Nodes & Paths – Different colors represent decision types (e.g., financial, operational, marketing) or different scenarios.
• Undo/Redo Buttons – Enables quick changes to scenario structures.
• Simulation Preview Mode – Allows USER (SC) to test how decisions impact the simulation in real-time.

How the GUI Editor Enhances UX
• Simplifies simulation design for non-technical users by eliminating coding.
• Reduces errors by using input validation and predefined logic templates.
• Improves engagement through an interactive, real-time visualization of business decisions.

Step 1.3: Input Validation and Simulation Logic Setup
• The interface automatically validates the inputs, ensuring there are no logical errors in the simulation design, such as conflicting decision branches or undefined variables. For example, if a USER (SC) forgets to connect a decision point to the corresponding outcomes, the system will prompt them to resolve the issue.
• USER (SC)s may define additional rules for branching logic, such as probabilistic rules for uncertain outcomes (e.g., the probability of success for a new product launch). These rules are used by the computational logic engine (102) to dynamically adjust simulation outcomes based on USER (SP) inputs and real-time data.

2. Executing the Simulation with Real-Time Feedback (102, 103)
Step 2.1: Decision Making and Real-Time Processing
• Once the simulation design is completed, the USER (SP) starts the simulation. At each decision point, the platform asks the USER (SP) to make a choice or take an action, such as adjusting product pricing or investing in new technology.
• As the USER (SP) makes decisions, the computational logic engine (102) processes these inputs in real-time, evaluates the corresponding outcomes, and dynamically updates the simulation’s state. The engine uses predefined algorithms, such as decision trees, to evaluate the consequences of each USER (SP) decision and adjust the simulation path accordingly.

Step 2.2: Dynamic Scenario Branching
• As the USER (SP) progresses through the simulation, the automated scenario branching module (103) dynamically modifies the simulation path based on USER (SP) decisions. This module ensures that each decision point (in some simulations and not in all) leads to different simulation branches, which reflect the possible outcomes of the USER (SP)’s choices.
• If the simulation involves uncertain events or external factors (e.g., market volatility or competitor actions), the scenario branching module may also incorporate probabilistic analysis to adjust outcomes based on these variables.

Step 2.3: Immediate Feedback and Adaptive Scoring
• After each decision, the adaptive feedback and scoring module (105) provides real-time feedback. The USER (SP) receives a performance score based on the effectiveness of their decisions and actions.
• The platform can offer suggestions for improvements, such as recommending a more aggressive marketing strategy if the USER (SP)’s sales performance is below expectations. The scoring system uses predefined performance metrics such as profitability, market share, and customer satisfaction to evaluate the USER (SP)’s performance in the simulation.

3. Managing Dependencies and Variables (104)
Step 3.1: Managing Parent-Child Relationships Between Variables
• The multi-layered dependency management system (104) tracks the relationships between interdependent variables within the simulation. When the USER (SP / SC) changes a key variable, such as the pricing of a product, the system automatically adjusts dependent variables, such as revenue, profit margins, and customer acquisition rates, to reflect the change.
• For example, if a USER (SP) decreases the price of a product, the system may automatically adjust variables like profit margin and market share based on the new pricing.

Step 3.2: Conflict Detection and Resolution
• If a USER (SP)’s decision results in conflicting changes to interdependent variables, the dependency management system uses hierarchical conflict resolution logic to automatically detect and resolve these conflicts. This ensures that the simulation remains consistent and that all dependent variables are adjusted appropriately.
• For example, if the USER (SP) simultaneously increases production capacity and cuts staff, the system would flag the potential conflict and prompt the USER (SP) to resolve it, ensuring that staffing levels align with production requirements.

4. Recursive Processing and Optimization (106)
Step 4.1: Storing and Reusing Computational Results
• To optimize performance, the recursive processing unit (106) stores intermediate results from previously executed simulation scenarios in a cache. This mechanism avoids redundant calculations and speeds up the processing time for future simulations that involve similar decisions or conditions.
• For example, if a USER (SP) runs the same simulation with slightly different input variables (e.g., a 10% increase in marketing budget), the system will use the cached results to avoid recalculating common elements, thus improving efficiency.

Step 4.2: Learning from USER (SP) Behaviour
• The recursive processing unit can incorporate machine learning techniques to identify patterns in USER (SP) decisions and outcomes. Over time, the system can adapt to USER (SP) preferences, suggesting adjustments based on past behaviours.
• For example, if a USER (SP) frequently invests heavily in product development but faces profitability issues, the system may learn to suggest a more balanced approach to budgeting in future simulations.

5. Unified Reporting and Performance Analysis (107)
Step 5.1: Consolidating Simulation Data
• The Unified Reporting System (107) aggregates and presents data from multiple scenarios of multiple simulation games, providing a comprehensive performance evaluation across diverse simulation games. This system consolidates USER (SP) decisions, outcomes, and performance scores, enabling users to analyze their strategic effectiveness across different game environments.
• Offering multi-dimensional analysis, the system allows users to compare decision-making performance under varying market conditions, business strategies, and functional competencies such as strategy planning, leadership, managerial, and functional skills.
• The reporting framework includes profitability reports, market share analysis, operational efficiency summaries, and comparative performance insights, giving Simulation Players (SPs) a deeper understanding of how their choices impact simulation outcomes.
• Simulation Creators (SCs) benefit from user behavior analytics, helping them refine simulation models for improved learning outcomes.
• Incorporating predictive analytics, the system leverages historical data to forecast trends and optimize future strategies, ensuring data-driven decision-making and enhanced business simulation performance.

Step 5.2: Multi-Dimensional and Predictive Analysis
• The reporting system allows USER (SP)s to perform multi-dimensional analysis by comparing different simulation scenarios, identifying patterns, and evaluating how various strategies perform under different conditions. For example, a USER (SP) can compare two different marketing strategies and assess which one resulted in higher customer acquisition and profitability.
• Additionally, the system can generate predictive analysis reports using historical data and USER (SP)-defined performance metrics, forecasting potential outcomes for future decision-making scenarios. This predictive feature enables USER (SP)s to evaluate the potential impact of long-term strategies, such as entering new markets or launching new products.

Step 5.3: Custom Recommendations and Performance Benchmarks
• The reporting system can also offer customized recommendations based on the USER (SP)’s performance in the simulation. These recommendations are customized to help the USER (SP) improve their decision-making process and optimize their strategy.
• The system may compare the USER (SP)’s outcomes with historical benchmarks, helping the USER (SP) understand how their performance stacks up against industry standards or past performance data.

6. Iteration and Refinement
Step 6.1: Adjusting the Simulation Design
• Based on the feedback and performance reports, the USER (SC) may choose to refine their simulation design. They can modify decision points, branching logic, and variable relationships to test new strategies and improve their performance.
• The iterative nature of the platform allows USER (SP)s to continuously experiment with different approaches, optimizing their simulations for more realistic or effective business modelling.

Step 6.2: Executing Multiple Paths
• To Users (SPs) can execute multiple paths in parallel, allowing them to explore a broader range of possibilities within the simulation. The system supports multi-threading, enabling concurrent processing of different strategic scenarios. This functionality allows users (SPs) to compare various decision paths simultaneously, assess potential outcomes, and refine their strategies based on real-time insights.

The method for performing the dynamic, no-code business simulation platform provides a seamless, interactive environment for creating, executing, and analyzing business simulations. The platform’s no-code interface, real-time feedback, dynamic branching, and strong dependency management ensure that USER (SP) can model complex business scenarios without the need for technical expertise. By optimizing performance through caching and machine learning, the platform also enhances efficiency and adapts to USER (SP) behaviour over time. With powerful reporting and performance analysis features, USER (SP)s gain deep insights into their strategies, empowering them to make informed decisions and improve their business practices.

Another method of performing the invention, as known to the applicant, involves the following steps and configurations:
1. No-Code USER (SC) Interface Setup
• The USER (SC) interface is implemented using React.js and JavaScript for the front end.
• A drag-and-drop builder allows USER (SC)s to define simulation scenarios, decision points, and outcomes.
• RESTful APIs facilitate communication between the front end and the back-end computational engine.
• The interface includes an input validation layer to prevent logical conflicts and ensure consistency.
• Example of Setup:
o Create a simulation template using the drag-and-drop interface.
o Define decision points and possible outcomes using pre-configured blocks.
o Set branching logic based on conditional statements and decision trees.

2. Computational Logic Engine Configuration
• The back-end computational logic engine is implemented using Python and C++.
• A decision tree algorithm is used to handle scenario branching.
• Recursive processing is enabled through a caching algorithm to reduce processing time.
• Multi-threading allows the engine to execute multiple simulations simultaneously.
• Example of Setup:
o Input simulation parameters (e.g., market demand, production capacity).
o Process the simulation data using the decision tree algorithm.
o Adjust the simulation outcome based on real-time input.

3. Automated Scenario Branching Execution
• The branching module uses conditional logic to select the next decision point based on USER (SP / SC) input.
• The system supports both deterministic and probabilistic branching.
• Example of Setup:
o If USER (SP / SC) decision = increase production capacity ? Outcome 1: Increased inventory.
o If USER (SP / SC) decision = maintain production capacity ? Outcome 2: Market shortage.
o If USER (SP / SC) decision = reduce production capacity ? Outcome 3: Reduced cost, but market loss.

4. Dependency Management Activation
• Variables are structured using a graph-based data model.
• Parent-child relationships are defined between dependent variables.
• If a parent variable changes, the system automatically adjusts all dependent child variables.
• Example of Setup:
o If raw material cost increases ? Adjust production cost ? Adjust pricing ? Adjust demand forecast.

5. Adaptive Feedback and Scoring Application
• The feedback module calculates scores based on USER (SP / SC) decisions and simulation outcomes.
• Weighting factors are assigned to different outcomes (e.g., profitability, efficiency).
• Real-time feedback is displayed through the interface.
• Example of Setup:
o Decision to increase production ? Score increases if demand matches supply ? Score decreases if inventory surplus occurs.
o Feedback: "Consider adjusting production to match projected demand."

6. Recursive Processing Utilization
• The recursive processing unit stores computed data and reuses it when similar scenarios arise.
• Improves computation efficiency by avoiding redundant calculations.
• Example of Setup:
o If price elasticity simulation repeats ? Retrieve stored results ? Adjust based on new demand parameters.

7. Reporting and Data Analysis Generation
• The reporting system consolidates simulation data across multiple scenarios.
• Reports are generated in real-time and displayed through dashboards.
• Export options include PDF, Excel, and CSV.
• Example of Setup:
o Performance report includes profitability, market share, and efficiency metrics.
o Comparative analysis across multiple USER (SP / SC) sessions.

8. Example of a Complete Simulation Workflow
1. USER (SP / SC) logs into the platform and selects a simulation template for supply chain management.
2. USER (SP / SC) defines initial parameters: production cost, demand forecast, lead time.
3. USER (SP / SC) sets branching logic:
o If demand > supply ? Increase production ? Increased costs.
o If demand < supply ? Reduce production ? Reduced costs but market loss.
4. USER (SP / SC) starts simulation.
5. Computational logic engine processes data in real-time.
6. Branching module adjusts decision paths based on USER (SP / SC) input.
7. Dependency management updates related variables automatically.
8. Recursive processing optimizes scenario calculation.
9. Adaptive feedback module provides real-time performance evaluation.
10. Reporting system generates a summary of outcomes and recommendations.
, Claims:WE CLAIM:

1. A dynamic no-code business simulation platform comprising:
- a no-code USER (SC) interface (101) configured to enable USER (SC)s to create and modify business simulation scenarios using a graphical interface without programming knowledge;
- a real-time computational logic engine (102) configured to process USER (SP) inputs and execute simulation logic in real-time;
- an automated scenario branching module (103) configured to adjust simulation paths dynamically based on USER (SP) input and real-time data;
- dynamic branching engine (103A) enables real-time scenario evolution by dynamically adjusting simulation pathways using conditional logic, probabilistic models, nested branching, and AI-driven adaptability
- a multi-layered dependency management system (104) configured to maintain consistency across interdependent simulation variables and automatically adjust dependent variables in response to changes in parent variables;
- a variable management module ensures real-time dependency mapping, automated data propagation, conflict resolution, and scenario synchronization for dynamic and consistent simulation outcomes
- adaptive feedback and scoring module (105) configured to compute real-time performance scores and provide feedback based on USER (SP) decisions and simulation outcomes;
- a recursive processing unit (106) configured to optimize simulation processing by storing and reusing previously computed results; and
- an unified reporting system (107) consolidates simulation data, generates performance reports, and enables predictive analytics for strategic evaluation;
wherein the simulation platform dynamically adjusts simulation paths and outcomes based on real-time USER (SP) decisions and feedback.

2. A no-code business simulation platform comprising:
- a no-code USER (SC) interface (101) configured to define simulation logic, decision points, and branching rules;
- a computational logic engine (102) configured to process simulation data in real-time;
- an adaptive scenario branching module (103) configured to modify simulation paths based on USER (SP) decisions;
- an adaptive feedback module (105) configured to provide real-time scoring and performance analysis; and
- a unified reporting system (107) configured to consolidates simulation data, generates performance reports, and enables predictive analytics for strategic evaluation;
wherein the platform allows USER (SC)s to create, execute, and modify business simulations without programming knowledge while adjusting simulation paths dynamically based on USER (SP) input.

3. The platform as claimed in Claim 1, wherein the no-code USER (SP / SC) interface (101) includes a graphical interface editor which includes but not limited to Drag-and-drop, Point-and-click, Dropdowns, Radio Buttons, Buttons, Text fields, Checkboxes etc., to define simulation logic and branching rules.

4. The platform as claimed in Claim 1, wherein the computational logic engine (102) uses a decision tree algorithm to evaluate USER (SP) decisions and adjust outcomes dynamically.

5. The platform as claimed in Claim 1, wherein the automated scenario branching module (103) adjusts branching outcomes based on probabilistic analysis and real-time input data.

6. The platform as claimed in Claim 1, wherein the dependency management system (104) establishes parent-child relationships between simulation variables and automatically adjusts dependent variables when parent variables change.

7. The platform as claimed in Claim 1, wherein the dependency management system (104) resolves conflicts between interdependent variables using hierarchical conflict resolution logic.

8. The platform as claimed in Claim 1, wherein the adaptive feedback and scoring module (105) assigns weighted scores to simulation outcomes based on predefined performance metrics.

9. The platform as claimed in Claim 1, wherein the adaptive feedback and scoring module (105) generates customized recommendations based on USER (SP) performance and simulation results.

10. The platform as claimed in Claim 1, wherein the recursive processing unit (106) stores intermediate computational results and reuses them to optimize subsequent scenario processing.

11. The platform as claimed in Claim 1, wherein the recursive processing unit (106) applies machine learning techniques to identify repetitive patterns in USER (SP / SC) decisions and adjust future outcomes accordingly.

12. The platform as claimed in Claim 1, wherein the unified reporting system (107) supports multi-dimensional analysis of simulation outcomes and comparative performance analysis across multiple scenarios.

13. The platform as claimed in Claim 1, wherein the unified reporting system (107) comprising predictive analytics to forecast future trends based on historical data, aiding in strategic decision-making.

14. The platform as claimed in Claim 1, wherein the no-code USER (SC) interface (101) includes an input validation layer to prevent logical conflicts in the simulation design.

15. The platform as claimed in Claim 1, wherein the computational logic engine (102) includes multi-threading support to process multiple simulations concurrently.

16. The platform as claimed in Claim 1, wherein the automated scenario branching module (103) includes a nested branching mechanism to allow multi-layered decision outcomes.

17. The platform as claimed in Claim 1, wherein the dependency management system (104) includes a monitoring system to detect changes in interdependent variables and automatically propagate adjustments.

18. The platform as claimed in Claim 1, wherein the adaptive feedback and scoring module (105) includes a performance benchmark system to compare USER (SP) outcomes with historical performance data.

19. The platform as claimed in Claim 1, wherein the recursive processing unit (106) includes a caching mechanism to store previously computed outcomes and optimize future simulations.

20. The platform as claimed in Claim 1, wherein the unified reporting system (107) generates predictive analysis reports based on historical data and USER (SC)-defined performance metrics.

Dated this 31st Day of March 2025