[upd] Refine energy cost calculations and enhance price adjustments in market and BDI agent logic. Implement rounding for improved accuracy in financial computations and adjust supply/demand thresholds for better market dynamics.

backend/core/ai.py

@@ -103,7 +103,7 @@ def get_energy_cost(resource_type: ResourceType) -> int:
         return 10
     energy_cost = abs(config.energy_cost)
     avg_output = max(1, (config.min_output + config.max_output) / 2) if config.output_resource else 1
-    return int(energy_cost / avg_output)
+    return int(round(energy_cost / avg_output))
 
 
 # Cached config values to avoid repeated lookups

backend/core/bdi/bdi_agent.py

@@ -438,6 +438,7 @@ class BDIAgentAI:
                     return self._create_gather_fallback(target_resource, reason, goal, plan, bdi_info)
                 
                 # Calculate quantity to buy
+                # Use max(1, ...) to avoid division by zero
                 can_afford = self.agent.money // max(1, order.price_per_unit)
                 space = self.agent.inventory_space()
                 quantity = min(2, can_afford, space, order.quantity)
@@ -540,7 +541,7 @@ class BDIAgentAI:
         min_price = getattr(economy, 'min_price', 100) if economy else 100
         
         energy_cost = get_energy_cost(resource_type)
-        fair_value = max(min_price, int(energy_cost * energy_to_money_ratio))
+        fair_value = max(min_price, int(round(energy_cost * energy_to_money_ratio)))
         
         # Apply trading skill
         sell_modifier = get_trade_price_modifier(self.skills.trading, is_buying=False)
@@ -549,15 +550,17 @@ class BDIAgentAI:
         signal = self.market.get_market_signal(resource_type)
         
         if signal == "sell":  # Scarcity
-            price = int(fair_value * 1.3 * sell_modifier)
+            price = int(round(fair_value * 1.3 * sell_modifier))
         elif signal == "hold":
-            price = int(fair_value * sell_modifier)
+            price = int(round(fair_value * sell_modifier))
         else:  # Surplus
             cheapest = self.market.get_cheapest_order(resource_type)
             if cheapest and cheapest.seller_id != self.agent.id:
-                price = max(min_price, cheapest.price_per_unit - 1)
+                # Undercut, but respect floor (80% of fair value or min_price)
+                floor_price = max(min_price, int(round(fair_value * 0.8)))
+                price = max(floor_price, cheapest.price_per_unit - 1)
             else:
-                price = int(fair_value * 0.8 * sell_modifier)
+                price = int(round(fair_value * 0.8 * sell_modifier))
         
         return max(min_price, price)
     

backend/core/goap/action.py

@@ -296,7 +296,7 @@ def create_buy_action(resource_type: ResourceType) -> GOAPAction:
 
     def cost(state: WorldState) -> float:
         # Trading cost is low (1 energy)
-        base_cost = 0.5
+        base_cost = 0.2
 
         # MILD profession effect for trading (everyone should be able to trade)
         # Traders get a bonus, but non-traders shouldn't be heavily penalized

backend/core/goap/actions.py

@@ -133,8 +133,8 @@ def _create_hunt() -> GOAPAction:
 
     def effects(state: WorldState) -> dict:
         # Account for success chance
-        effective_meat = int(expected * config.success_chance)
+        effective_meat = int(round(expected * config.success_chance))
-        effective_hide = int(1 * config.success_chance)  # Average hide
+        effective_hide = int(round(1 * config.success_chance))  # Average hide
 
         energy_spent = abs(config.energy_cost) / 50.0
 
@@ -295,7 +295,7 @@ def _create_sell_action(resource_type: ResourceType, min_keep: int = 1) -> GOAPA
     def effects(state: WorldState) -> dict:
         # Estimate we'll get a reasonable price (around min_price from config)
         # This is approximate - actual execution will get real prices
-        estimated_price = 100  # Base estimate (min_price from config)
+        estimated_price = 150  # Better estimate than min_price (fair value)
 
         current = getattr(state, count_name)
         sell_qty = min(3, current - min_keep)  # Sell up to 3, keep minimum

backend/core/market.py

@@ -63,7 +63,7 @@ class Order:
     def apply_discount(self, percentage: float = 0.1) -> None:
         """Apply a discount to the price."""
         min_price = _get_min_price()
-        reduction = max(1, int(self.price_per_unit * percentage))
+        reduction = max(1, int(round(self.price_per_unit * percentage)))
         self.price_per_unit = max(min_price, self.price_per_unit - reduction)
 
     def adjust_price(self, new_price: int, current_turn: int) -> bool:
@@ -154,9 +154,9 @@ class OrderBook:
     DISCOUNT_RATE: float = 0.12
 
     # Supply/demand thresholds
-    LOW_SUPPLY_THRESHOLD: int = 3  # Less than this = scarcity
+    LOW_SUPPLY_THRESHOLD: int = 10  # Less than this = scarcity
-    HIGH_SUPPLY_THRESHOLD: int = 10  # More than this = surplus
+    HIGH_SUPPLY_THRESHOLD: int = 50  # More than this = surplus
-    DEMAND_DECAY: float = 0.95  # How fast demand score decays per turn
+    DEMAND_DECAY: float = 0.99  # How fast demand score decays per turn
 
     def __post_init__(self):
         """Initialize price history and load config values."""
@@ -266,7 +266,8 @@ class OrderBook:
         total_cost = actual_quantity * order.price_per_unit
         if buyer_money < total_cost:
             # Try to buy what they can afford
-            actual_quantity = buyer_money // order.price_per_unit
+            # Use max(1, ...) to avoid division by zero, though price is min 100
+            actual_quantity = buyer_money // max(1, order.price_per_unit)
             if actual_quantity <= 0:
                 return TradeResult(
                     success=False,
@@ -376,7 +377,7 @@ class OrderBook:
         # Use average sale price as reference if available
         reference_price = base_price
         if history.avg_sale_price > 0:
-            reference_price = int((base_price + history.avg_sale_price) / 2)
+            reference_price = int(round((base_price + history.avg_sale_price) / 2))
 
         # Adjust based on supply/demand
         if ratio < 0.7:  # Scarcity - raise price
@@ -387,7 +388,7 @@ class OrderBook:
         else:
             price_multiplier = 1.0
 
-        suggested = int(reference_price * price_multiplier)
+        suggested = int(round(reference_price * price_multiplier))
         return max(_get_min_price(), suggested)
 
     def adjust_order_price(self, order_id: str, seller_id: str, new_price: int, current_turn: int) -> bool: