In the hallowed, fluorescent-lit halls of our engineering department, where the air hums with the gentle whir of server fans and the scent of burnt coffee, a truly terrible idea was born. It began, as most terrible ideas do, with a simple question posed during a late-night coding session: "What if Shakespeare didn't have a quill, but a compiler?" This led to another, more specific, and infinitely more cursed question: "Could you capture the tragic essence of Hamlet using the cold, calculating logic of MATLAB?" We decided, against our better judgment and the advice of every liberal arts major we knew, that the answer had to be yes.
The goal was not a literal translation of iambic pentameter into matrix operations. That would be impossible and, frankly, even more pointless than what we actually did. Instead, we set out to create a procedural re-imagining of the play. We aimed to model the characters, their fatal flaws, their motivations, and the inexorable chain of events that leads to a stage littered with bodies, all within a single, executable .m file. We sought to prove that the core of a tragedy isn't the poetry, but the algorithm—a deterministic sequence of inputs and state changes that can only lead to one catastrophic output. The result was a script that is both a masterpiece of poor decisions and a surprisingly poignant reflection on the nature of fate. It is, in short, hilariously tragic.
Before a single line of code could be written, we had to define the engineering problem. The input was clear: one (1) sixteenth-century Elizabethan tragedy, The Tragedy of Hamlet, Prince of Denmark. The required output was a MATLAB script that, when run, would simulate the play's plot, culminating in the deaths of most major characters. The primary challenge was abstracting the deeply human and poetic elements of the play into quantifiable variables and logical structures. We needed to deconstruct the very soul of the story into a set of system requirements.
Our first task was to define the system's state variables. The characters themselves were the obvious place to start. Each character would be a data structure, a struct in MATLAB's parlance, containing key attributes. For example, every character needed a boolean flag: isAlive. They also needed psychological state variables, like sanity, represented as a floating-point number from 1.0 (perfectly stable) down to 0.0 (complete breakdown), and suspicion, a metric for tracking their distrust of others. Plot-critical information also had to be stored, such as Hamlet.knowsClaudiusIsKiller, a boolean that starts as false and acts as the primary catalyst for the entire program. The tragedy itself, we determined, is simply a cascade of state changes. The ghost appears, a flag is flipped. A play is staged, a guilt variable is incremented. A poisoned sword is introduced, a character's isPoisoned attribute is set to true. The elegance of the tragedy lies in its brutal, unforgiving logic, a quality that felt right at home in a programming environment.
With the problem defined, we began architecting our solution. The entire simulation would be encapsulated in a main script, run_elsinore.m. This script would initialize the environment, define the character structs, and then call a series of functions that represented the major acts and scenes of the play. The structure needed to be linear and deterministic, mirroring the inescapable nature of Hamlet's fate. There would be no random chance, no user input. Once you hit 'Run', the tragedy was set in motion, and nothing could stop the inevitable conclusion.
We chose MATLAB structs to represent our cast of characters. This allowed us to group all relevant properties under a single variable name. The initial setup looked something like this: Hamlet.isAlive = true; Hamlet.sanity = 0.8; % Initial state: Brooding but functional., Claudius.isKing = true; Claudius.guiltLevel = 10.0; % Max guilt, but hidden., and Ophelia.loveInterest = 'Hamlet'; Ophelia.sanity = 1.0; % Initial state: Tragically stable.. The entire court of Denmark was initialized in a function called initialize_court_state(), which effectively set the stage for Act I. The functions that followed were named for their dramatic purpose: trigger_ghostly_apparition(), execute_play_within_a_play(), and the ominously titled initiate_final_duel_protocol(). The core of our work was writing the logic inside these functions to manipulate the character structs in a way that followed Shakespeare's deadly narrative arc.
Our coding process followed the play's five-act structure. Act I was initialization. We created the Claudius struct, setting his isKing flag to true and linking him to the Gertrude struct via a spouse attribute. We then wrote the trigger_ghostly_apparition function. This function's sole purpose was to find the Hamlet struct and update his internal state. Specifically, it set Hamlet.knowsClaudiusIsKiller = true; and Hamlet.revengePending = true;. We added a crucial comment here: % WARNING: Interaction with spectral entities may lead to high resource utilization in sanity module and unpredictable behavior.. Act II was dedicated to Hamlet's famous indecision. We modeled this with a while loop: while Hamlet.revengePending && Hamlet.isIndecisive. Inside this loop, Hamlet would perform non-essential tasks, like talking to Rosencrantz and Guildenstern (defined as low-priority background processes) or running a contemplate_existence() function that simply paused the script for a few seconds before returning. This loop was the heart of the tragedy's delay, a programmatic representation of procrastination. Act III saw the implementation of execute_play_within_a_play(). This function took Claudius as an input and monitored his guiltLevel reaction. If his reaction spiked, the function would return true, confirming his guilt to Hamlet and finally allowing him to break out of his indecision loop. This act also contained our first fatal error: the death of Polonius. We coded this as an unfortunate case of mistaken identity: if person_behind_arras == 'Claudius'; kill(person); else; kill(person); % Execute anyway, log error as collateral damage. end;. This event had a cascading effect, as it sent Ophelia's sanity variable into a steep decline. Acts IV and V were a rapid sequence of function calls where everything falls apart. Ophelia's sanity dropping below a certain threshold triggered a fatal_drowning_event(). The final duel was a function that systematically set the isAlive flags of Laertes, Gertrude, Claudius, and finally Hamlet to false, with appropriate pauses and console outputs describing the grim events.
The true dark humor of the project emerged in the code itself, particularly in the comments. We wrote the script as if it were a serious piece of enterprise software, with all the jargon and dry warnings that entails. The juxtaposition of corporate-speak and high tragedy was where we found our comedy.
Here is a snippet from our character initialization file, characters.m: `matlab % ========================================================================= % CHARACTER OBJECT INITIALIZATION: ELSINORE CAST % Version 1.0 % Last Updated: 2023-10-27 % =========================================================================
% --- Initialize Prince Hamlet --- Hamlet.name = 'Hamlet, Prince of Denmark'; Hamlet.isAlive = true; Hamlet.isHeirToThrone = true; Hamlet.sanity = 0.8; % NOTE: Initial value is below baseline. Exhibits brooding behavior. Hamlet.revengePending = false; % Flag to be triggered by external ghost module. Hamlet.existentialCrisisActive = true; % Default state.
% --- Initialize King Claudius --- Claudius.name = 'Claudius, King of Denmark'; Claudius.isAlive = true; Claudius.isKing = true; Claudius.guiltLevel = 10.0; % Set to maximum. System must not expose this value publicly. Claudius.isDrinking = true; % Constant state.
% --- Initialize Ophelia --- Ophelia.name = 'Ophelia'; Ophelia.isAlive = true; Ophelia.sanity = 1.0; % Initial state is stable. Ophelia.father = 'Polonius'; Ophelia.loveInterest = 'Hamlet'; % WARNING: Pointer to unstable object. High risk of cascading failure. `
The function for the play-within-a-play, "The Mousetrap," was another highlight. It was designed as a diagnostic tool to test an external system—in this case, King Claudius. `matlab function guiltConfirmed = execute_play_within_a_play(targetCharacter) % This function stages a performance to check for a guilt response. % It serves as a callback to validate the data from the ghost module.
fprintf('Running diagnostic: "The Mousetrap"...\n');
% Simulate the play's key scene. simulatedPoisoningEvent();
% Monitor target for emotional output spike. if targetCharacter.guiltLevel > 9.5 && rand() > 0.1 fprintf('RESPONSE DETECTED. Target has fled the scene. Guilt confirmed.\n'); guiltConfirmed = true; % The king's guilt is a global variable of sorts. setGlobalFlag('ClaudiusIsGuilty', true); else fprintf('No significant response. Diagnostic inconclusive.\n'); guiltConfirmed = false; end end `
The final scene was the most brutal, a function that was essentially a cleanup script, terminating all remaining processes. `matlab function execute_final_duel_protocol(Hamlet, Laertes, Claudius, Gertrude) % INITIATING FINAL SCENE. ALL PROCESSES WILL BE TERMINATED. fprintf('Executing final duel protocol. Unsaved work will be lost.\n');
% Event 1: Poisoned sword. Laertes.sword.isPoisoned = true; % Scuffle ensues. Both Hamlet and Laertes are now flagged. Hamlet.isPoisoned = true; Laertes.isPoisoned = true; fprintf('Error: Hamlet and Laertes have been fatally poisoned.\n');
% Event 2: Poisoned wine. Gertrude.isPoisoned = true; Gertrude.isAlive = false; % Terminating Gertrude process. fprintf('FATAL EXCEPTION: Gertrude has consumed poisoned beverage. Process terminated.\n');
% Event 3: Revenge execution. Claudius.isPoisoned = true; Claudius.isAlive = false; % Terminating Claudius process. fprintf('System Alert: King Claudius process terminated by user Hamlet.\n');
% Final termination. Hamlet.isAlive = false; % Terminating Hamlet process. fprintf('Main process Hamlet has concluded. The rest is silence.\n');
% System cleanup. clear all; close all; clc; end `
Once our basic script was running flawlessly—that is to say, once it reliably killed everyone—we began to consider more advanced implementations. What if we moved beyond simple scripts and structs and embraced more complex MATLAB features? The first idea was to refactor the entire project using Object-Oriented Programming. Each character could be a class. The Hamlet class would inherit from a TragicHero superclass, which would come with built-in methods like .soliloquize() and .suffer(). The destructor method for the Hamlet object would, of course, print "The rest is silence..." to the command window upon his death. This would offer a much cleaner, more encapsulated way to manage the state and behavior of each character.
Then, we imagined the sheer, beautiful horror of modeling the plot in Simulink. The entire tragedy could be represented as a block diagram. The Ghost would be a signal generator, sending a square wave pulse to the Hamlet subsystem. Hamlet's indecision would be a massive feedback loop, where the output of his contemplate_existence block is fed back into its input, preventing the signal from moving forward. The final duel would be a horrifying cascade of logic gates and switches, where one false signal from Gertrude.isAlive triggers a state change in Laertes, which in turn triggers the final termination of Hamlet and Claudius. It would be a visual representation of fate, a flowchart of doom. You could literally watch the signals of life extinguish one by one across the diagram.
The most dystopian idea, however, was to apply machine learning. We could create a dataset from the events of the play, with features like indecision_duration, parental_guilt_level, and number_of_ghostly_apparitions. The target variable would be number_of_deaths. We could then train a regression model to predict the final body count. The model, after training on this single, tragic data point, would of course become perfectly overfitted. It would predict a body count of eight with 100% confidence for any input resembling the state of Denmark. It would be a machine learning model that has learned the one and only lesson Hamlet has to teach: given these inputs, the system will always fail catastrophically.
In the end, this project was a journey into the heart of algorithmic darkness. We took one of the most beautiful, complex works of human art and stripped it down to its cold, logical bones. The result was a script that, in its own strange way, was a tribute to the genius of the original. It proved that the structure of tragedy is so robust, so mathematically sound, that it can survive translation into even the most soulless and sterile of languages. Our code ran without errors. It executed its purpose with brutal efficiency. And as the final line printed to the console and the workspace was cleared, we were left with a profound and unsettling feeling: our program was a complete success, and therefore, an absolute tragedy.
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