Genre: Simulation / Tycoon

Optimization, systems mastery, and satisfying feedback loops define management games.

NEVER Do (Expert Anti-Patterns)

Simulation & Economy

• NEVER use floating-point for primary currency; strictly use Integer Cents (or fixed-point math) to prevent accumulated precision errors in financial models.
• NEVER process 1000+ entities individually in _process(); strictly use a Tick Manager to batch updates or process entities in rotating pools.
• NEVER rely on linear cost scaling; strictly use Exponential Growth (Base * pow(1.15, Level)) to maintain challenge and strategic tension.
• NEVER hide critical metrics from the player; strictly provide Detailed Breakdowns (Income vs. Expense) so players can make optimization-based decisions.
• NEVER allow infinite resource stacking; strictly enforce Logistical Caps (warehouses/silos) to create meaningful space-management gameplay loops.
• NEVER let the early game become a "Waiting Simulator"; strictly Front-Load Decisions and quick early wins to build player momentum.
• NEVER modify a shared Resource directly; strictly use duplicate() to avoid unintentionally updating every building of that type.
• NEVER tie simulation logic to the visual framerate; strictly use _physics_process() or delta accumulators for deterministic simulation results.

Performance & Threading

• NEVER update UI labels every frame; strictly use Event-Driven Signals to refresh UI ONLY when the underlying data changes.
• NEVER run heavy economic loops synchronously; strictly use WorkerThreadPool to offload complex calculations and prevent UI stutters.
• NEVER store massive resource data as Nodes; strictly use RefCounted or Data Resources to avoid the memory/CPU overhead of the SceneTree.
• NEVER ignore OS.low_processor_usage_mode; strictly enable it for stationary management screens to save massive CPU/Battery life.
• NEVER manipulate the SceneTree from background threads; strictly use call_deferred() for thread-safe UI updates.
• NEVER parse large JSON save files on the main thread; strictly use Threaded Serialization or optimized binary .res formats.
• NEVER use standard equality (==) for needs; strictly use is_equal_approx() to prevent floating-point jitter failures in logic gates.

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🛠 Expert Components (scripts/)

Original Expert Patterns

• sim_tick_manager.gd - Variable-speed tick system decoupling simulation from rendering.
• tycoon_economy.gd - Multi-resource economic engine with integer-precision currency.

Modular Components

• simulation_patterns.gd - Reusable patterns: AStarGrid2D logistics and low-processor modes.

---

Economy Design

The heart of any tycoon game is its economy. Key principle: multiple interconnected resources that force trade-offs.

Multi-Resource System

class_name TycoonEconomy
extends Node

signal resource_changed(resource_type: String, amount: float)
signal went_bankrupt

var resources: Dictionary = {
    "money": 10000.0,
    "reputation": 50.0,  # 0-100
    "workers": 0,
    "materials": 100.0,
    "energy": 100.0
}

var resource_caps: Dictionary = {
    "reputation": 100.0,
    "workers": 50,
    "energy": 1000.0
}

func modify_resource(type: String, amount: float) -> bool:
    if amount < 0 and resources[type] + amount < 0:
        if type == "money":
            went_bankrupt.emit()
        return false  # Can't go negative
    
    resources[type] = clamp(
        resources[type] + amount,
        0,
        resource_caps.get(type, INF)
    )
    resource_changed.emit(type, resources[type])
    return true

Income/Expense Tracking

class_name FinancialTracker
extends Node

var income_sources: Dictionary = {}  # source_name: amount_per_tick
var expense_sources: Dictionary = {}

signal financial_update(profit: float, income: float, expenses: float)

func calculate_tick() -> float:
    var total_income := 0.0
    var total_expenses := 0.0
    
    for source in income_sources.values():
        total_income += source
    
    for source in expense_sources.values():
        total_expenses += source
    
    var profit := total_income - total_expenses
    financial_update.emit(profit, total_income, total_expenses)
    return profit

---

Time System

Simulation games need controllable time:

class_name SimulationTime
extends Node

signal time_tick(delta_game_hours: float)
signal day_changed(day: int)
signal speed_changed(new_speed: int)

enum Speed { PAUSED, NORMAL, FAST, ULTRA }

@export var seconds_per_game_hour := 30.0  # Real seconds

var current_speed := Speed.NORMAL
var speed_multipliers := {
    Speed.PAUSED: 0.0,
    Speed.NORMAL: 1.0,
    Speed.FAST: 3.0,
    Speed.ULTRA: 10.0
}

var current_hour := 8.0  # Start at 8 AM
var current_day := 1

func _process(delta: float) -> void:
    if current_speed == Speed.PAUSED:
        return
    
    var game_delta := (delta / seconds_per_game_hour) * speed_multipliers[current_speed]
    current_hour += game_delta
    
    if current_hour >= 24.0:
        current_hour -= 24.0
        current_day += 1
        day_changed.emit(current_day)
    
    time_tick.emit(game_delta)

func set_speed(speed: Speed) -> void:
    current_speed = speed
    speed_changed.emit(speed)

---

Entity Management

Workers/NPCs

class_name Worker
extends Node

enum State { IDLE, WORKING, RESTING, COMMUTING }

@export var wage_per_hour: float = 10.0
@export var skill_level: float = 1.0  # Productivity multiplier
@export var morale: float = 80.0  # 0-100

var current_state := State.IDLE
var assigned_workstation: Workstation

func update(game_hours: float) -> void:
    match current_state:
        State.WORKING:
            if assigned_workstation:
                var productivity := skill_level * (morale / 100.0)
                assigned_workstation.work(game_hours * productivity)
                morale -= game_hours * 0.5  # Working tires workers
        State.RESTING:
            morale = min(100.0, morale + game_hours * 2.0)

func calculate_hourly_cost() -> float:
    return wage_per_hour

Buildings/Facilities

class_name Facility
extends Node3D

@export var build_cost: Dictionary  # resource_type: amount
@export var operating_cost_per_hour: float = 5.0
@export var capacity: int = 5
@export var output_per_hour: Dictionary  # resource_type: amount

var assigned_workers: Array[Worker] = []
var is_operational := true
var efficiency := 1.0

func calculate_output(game_hours: float) -> Dictionary:
    if not is_operational or assigned_workers.is_empty():
        return {}
    
    var worker_efficiency := 0.0
    for worker in assigned_workers:
        worker_efficiency += worker.skill_level * (worker.morale / 100.0)
    worker_efficiency /= capacity  # Normalize to 0-1
    
    var result := {}
    for resource in output_per_hour:
        result[resource] = output_per_hour[resource] * game_hours * worker_efficiency * efficiency
    return result

---

Customer/Demand System

class_name CustomerSimulation
extends Node

@export var base_customers_per_hour := 10.0
@export var demand_curve: Curve  # Hour of day vs demand multiplier

var customer_queue: Array[Customer] = []

func generate_customers(game_hour: float, delta_hours: float) -> void:
    var demand_mult := demand_curve.sample(game_hour / 24.0)
    var reputation_mult := Economy.resources["reputation"] / 50.0  # 100 rep = 2x customers
    
    var customers_to_spawn := base_customers_per_hour * delta_hours * demand_mult * reputation_mult
    
    for i in int(customers_to_spawn):
        spawn_customer()

func spawn_customer() -> void:
    var customer := Customer.new()
    customer.patience = randf_range(30.0, 120.0)  # Seconds before leaving
    customer.spending_budget = randf_range(10.0, 100.0)
    customer_queue.append(customer)

---

Feedback Systems

Visual Feedback

# Money flying to bank, resources flowing, etc.
class_name ResourceFlowVisualizer
extends Node

func show_income(amount: float, from: Vector2, to: Vector2) -> void:
    var coin := coin_scene.instantiate()
    coin.position = from
    add_child(coin)
    
    var tween := create_tween()
    tween.tween_property(coin, "position", to, 0.5)
    tween.tween_callback(coin.queue_free)
    
    var label := Label.new()
    label.text = "+$" + str(int(amount))
    label.position = from
    add_child(label)
    
    var label_tween := create_tween()
    label_tween.tween_property(label, "position:y", label.position.y - 30, 0.5)
    label_tween.parallel().tween_property(label, "modulate:a", 0.0, 0.5)
    label_tween.tween_callback(label.queue_free)

Statistics Dashboard

class_name StatsDashboard
extends Control

@export var graph_history_hours := 24
var income_history: Array[float] = []
var expense_history: Array[float] = []

func record_financial_tick(income: float, expenses: float) -> void:
    income_history.append(income)
    expense_history.append(expenses)
    
    # Keep last N entries
    while income_history.size() > graph_history_hours:
        income_history.pop_front()
        expense_history.pop_front()
    
    queue_redraw()

func _draw() -> void:
    # Draw income/expense graph
    draw_line_graph(income_history, Color.GREEN)
    draw_line_graph(expense_history, Color.RED)

---

Progression & Unlocks

class_name UnlockSystem
extends Node

var unlocks: Dictionary = {
    "basic_facility": true,
    "advanced_facility": false,
    "marketing": false,
    "automation": false
}

var unlock_conditions: Dictionary = {
    "advanced_facility": {"money_earned": 50000},
    "marketing": {"reputation": 70},
    "automation": {"workers_hired": 20}
}

var progress: Dictionary = {
    "money_earned": 0.0,
    "workers_hired": 0
}

func check_unlocks() -> Array[String]:
    var newly_unlocked: Array[String] = []
    
    for unlock in unlock_conditions:
        if unlocks[unlock]:
            continue  # Already unlocked
        
        var conditions := unlock_conditions[unlock]
        var all_met := true
        
        for condition in conditions:
            if progress.get(condition, 0) < conditions[condition]:
                all_met = false
                break
        
        if all_met:
            unlocks[unlock] = true
            newly_unlocked.append(unlock)
    
    return newly_unlocked

---

Common Pitfalls

Pitfall	Solution
Economy too easy to break	Extensive balancing, soft caps, diminishing returns
Boring early game	Front-load interesting decisions, quick early progression
Information overload	Progressive disclosure, collapsible UI panels
No clear goals	Milestones, achievements, scenarios
Tedious micromanagement	Automation unlocks, batch operations

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Godot-Specific Tips

1. UI: Use Control nodes extensively, Tree for lists, GraphEdit for connections
2. Performance: Process entities in batches, not every frame
3. Save/Load: Convert all game state to Dictionary for JSON serialization
4. Isometric view: Use Camera2D with orthographic projection

Reference

• Master Skill: godot-master