diff --git a/human_machine/decision_support.py b/human_machine/decision_support.py
index ad35e5a..c93bbaa 100644
--- a/human_machine/decision_support.py
+++ b/human_machine/decision_support.py
@@ -1 +1,63 @@
-print("Hello World")
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Dict, Any, Callable, Optional
+
+
+@dataclass
+class Decision:
+    """Represents a decision with a set of options and a selected recommendation.
+
+    Attributes
+    ----------
+    options : Dict[str, Any]
+        A mapping of option names to their underlying values.
+    recommendation : Optional[str]
+        The name of the option that is currently recommended. None if no
+        recommendation is available.
+    """
+    options: Dict[str, Any]
+    recommendation: Optional[str] = None
+
+
+class DecisionSupport:
+    """
+    Simple decision support system that ranks options based on a scoring function.
+
+    A `scorer` callable is provided to map option values to numeric scores. The
+    `evaluate` method selects the option with the highest score.
+    """
+
+    def __init__(self, scorer: Callable[[Any], float]) -> None:
+        self.scorer = scorer
+
+    def evaluate(self, options: Dict[str, Any]) -> Decision:
+        """
+        Evaluate and recommend the option with the highest score.
+
+        Parameters
+        ----------
+        options : Dict[str, Any]
+            A mapping from option names to their raw values.
+
+        Returns
+        -------
+        Decision
+            A Decision object containing the original options and the recommended key.
+        """
+        if not options:
+            return Decision(options, None)
+        scores = {name: self.scorer(val) for name, val in options.items()}
+        best = max(scores, key=scores.get)
+        return Decision(options, best)
+
+
+if __name__ == "__main__":
+    # Example: choose the largest number
+    def identity_score(x: float) -> float:
+        return x
+
+    ds = DecisionSupport(identity_score)
+    opts = {"A": 0.5, "B": 0.8, "C": 0.3}
+    result = ds.evaluate(opts)
+    print("Recommendation:", result.recommendation)